Systems and methods for process design and analysis

ABSTRACT

Systems and methods for process design and analysis of processes that result in products or analytical information are provided. A hypergraph data store is maintained and comprises versions of each process. A version comprises a hypergraph with nodes, for stages of the process, and edges. Stages have parameterized resource inputs associated with stage input properties, and input specification limits. Stages have resource outputs with output properties and output specification limits. Edges link the outputs of nodes to the inputs of other nodes. A run data store is maintained with a plurality of process runs, each run identifying a process version, values for the inputs of nodes in the corresponding hypergraph, their input properties, resource outputs of the nodes, and obtained values of output properties of the resource outputs.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.15/690,128, filed Aug. 29, 2017, entitled “Systems and Methods forProcess Design and Analysis,” which claims priority to U.S. patentapplication Ser. No. 14/801,650, filed Jul. 16, 2015, entitled “Systemsand Methods for Process Design and Analysis,” which claims priority toU.S. Provisional Application No. 62/032,217, filed Aug. 1, 2014,entitled “Computer-Implemented Method for Recording and AnalyzingScientific Test Procedures and Data,” and U.S. Provisional ApplicationNo. 62/184,556, filed Jun. 25, 2015, entitled “Computer-ImplementedMethod for Recording and Analyzing Scientific Test Procedures and Data,”each of which is hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates generally to systems and methods forprocess design and analysis of processes that result in analyticalinformation or products.

BACKGROUND

Multi-stage processes are relied upon in the research and manufacture ofa wide range of products including biologics, pharmaceuticals,mechanical devices, electrical devices, and food, to name a fewexamples. Unfortunately, such processes typically have many sources ofvariation. While most of these sources are minor and may be ignored, thedominant sources of variation may adversely affect the efficiency oreven viability of such processes. If identified, however, resources toremove these dominant sources of variation can be engaged and,potentially, such dominant sources of variation can be removed,minimized or contained. Once these dominant sources of variation areaddressed, a process may be considered stabilized. When a process isstable, its variation should remain within a known set of limits. Thatis, at least, until another assignable source of variation occurs. Forexample, a laundry soap packaging line may be designed to fill eachlaundry soap box with fourteen ounces of laundry soap. Some boxes willhave slightly more than fourteen ounces, and some will have slightlyless. When the package weights are measured, the data will demonstrate adistribution of net weights. If the production process, its inputs, orits environment (for example, the machines on the line) change, thedistribution of the data will change. For example, as the cams andpulleys of the machinery wear, the laundry soap filling machine may putmore than the specified amount of soap into each box. Although thismight benefit the customer, from the manufacturer's point of view, thisis wasteful and increases the cost of production. If the manufacturerfinds the change and its source in a timely manner, the change can becorrected (for example, the cams and pulleys replaced),

While identification of variation of processes is nice in theory, inpractice there are many barriers to finding such variation. Mostprocesses combine many different functional components each with theirown data forms and types of errors. For instance, a process formanufacturing a synthetic compound using a cell culture combineschemical components, biological components, fermentation components, andindustrial equipment components. Each of these components involvesdifferent units of quantification, measurement, and error. As such, therate-limiting step for developing and stabilizing processes is notdevelopment of the algorithms that are used in such processes; it is theacquisition and contextualizing of the data in such processes. Thisrequires data aggregation and reproducibility assessment across manydisparate systems and functionalities so that scientific reasoning isbased on reproducible data rather than on artifacts of noise anduncertainty. Conventional systems fail to deliver adequate capabilitiesfor such analysis. They focus on storing files and data withoutproviding the structure, context or flexibility to enable real-timeanalytics and feedback to the user.

For instance, electronic lab notebooks (ELNs) are basically “paper onglass” and have inadequate ability to streamline longitudinal analyticsacross studies. Lab information management systems (LIMS) focus onsample data collection, but don't provide the protocol or study contextto facilitate analytics, nor the flexibility to adapt to changingworkflows “on-the-fly” and the many disparate functionalities that areoften found in processes. Thus the relationship between protocol andoutcome remains unclear or even inaccessible and information systemsbecome “dead” archives of old work mandated by institutional policiesrather than assets that drive process stabilization.

As a result, billions of dollars are lost each year on material and lifescience research that are not stabilized and thus have unsatisfactoryreproducibility rates. Moreover, the incidence of multi-million dollarfailures during process transfer to manufacturing remains high. Thus,given the above background, what is needed in the art are improvedsystems and methods for process design and analysis of processes thatresult in their stabilization.

SUMMARY

The disclosed embodiments address the need in the art for improvedsystems and methods for stabilization of processes that result inanalytical information or products. As used herein the term “product”refers to, for example, tangible products such as materials,compositions, ingredients, medicines, bulk materials, and the like; andthe term “analytical information” refers to, for example, categorical orquantitative data describing measurements of materials, equipment, orprocess settings. The disclosed systems and methods advantageously anduniquely reduce experimental noise and collaborative friction fromresearch and development to manufacturing. The disclosed systems andmethods facilitate visualization of data against evolving maps ofexperimental processes to highlight quality issues and opportunities,expose trends and causal relationships across time, experiments andteams, stimulate collaborative improvement of experimental and processquality, and stabilize processes.

The disclosed systems and methods maintain a hypergraph data store whichhas one or more versions of one or more processes. A version of aprocess comprises a hypergraph with nodes, for stages of the process,and edges. Stages have parameterized resource inputs associated withstage input properties, and input specification limits. Stages haveresource outputs with output properties and output specification limits.Edges link the outputs of nodes to the inputs of other nodes,representing the intended or actual transfer of resources from output toinput.

The disclosed systems and methods also maintain a run data store havinga plurality of process runs. Each process run identifies a processversion, values for the inputs of a first node in the hypergraph of thecorresponding process, their input properties, the resource outputs ofthe first node, and obtained values of output properties of the resourceoutputs. When a query identifies one or more inputs and/or outputspresent in the run data store, they are formatted for analysis.

Now that a general summary of the disclosed systems and methods has beenoutlined, more specific embodiments of the disclosed systems and methodswill be presented.

One aspect of the present disclosure provides a non-transitory computerreadable storage medium for analyzing one or more processes. Eachprocess in the one or more processes results in a respective product oranalytical information. The non-transitory computer readable storagemedium stores instructions, which when executed by a first device, causethe first device to maintain a hypergraph data store and a run datastore, and to execute instructions for acquiring and processing data forthe one or more processes.

In this aspect, the hypergraph data store comprises, for each respectiveprocess in the one or more processes, a respective plurality of versionsof the respective process. Each respective version comprises ahypergraph comprising a plurality of nodes connected by edges in aplurality of edges. Each respective node in the plurality of nodesrepresents a respective stage in the respective process. Further, eachnode in the plurality of nodes is associated with a set of parameterizedresource inputs to the respective stage in the corresponding process,and/or a set of parameterized resource outputs to the respective stagein the corresponding process. Each respective edge in the plurality ofedges specifies that the set of parameterized resource outputs of a nodein the plurality of nodes is included in the set of parameterizedresource inputs of at least one other node in the plurality of nodes.

In this aspect, the run data store comprises a plurality of processruns. Each process run comprises an identification of a version in theplurality of versions for a process in the one or more processes. Eachprocess run further comprises values for the respective set ofparameterized resource inputs of a first node in the hypergraph of therespective version. Each process run further comprises the respectiveset of parameterized resource outputs of the first node in thehypergraph of the respective version. Each process run further comprisesobtained values of at least one output property of a parameterizedresource output in the respective set of parameterized resource outputsof the first node in the hypergraph of the respective version.

In some alternative embodiments, the instructions for acquiring andprocessing data for the one or more processes comprises executing a datadriver for a respective process in the one or more processes. The datadriver includes instructions for receiving a dataset for the respectiveprocess and instructions for processing the dataset. The instructionsfor processing the dataset comprise instructions for parsing the datasetto obtain an identification of a process run in the run data store. Theinstructions for processing the dataset further comprise instructionsfor parsing the dataset to obtain input and/or output property valuesassociated with a respective set of parameterized resource inputs and/oroutputs of a first node in the hypergraph of the respective process forthe process run. The instructions for processing the dataset furthercomprise instructions for populating the input and/or output propertyvalues of parameterized resource inputs and/or outputs of the first nodein the run data store with the parsed values.

In some embodiments, the instructions for acquiring and processing datacomprise instructions for generating or changing one or moreparameterized resource inputs, parameterized resource outputs, processruns, stages, nodes, edges, input properties, output properties, inputspecification limits of input properties, output specification limits ofoutput properties and/or obtained values of input or output propertiespresent in the run data store based on the acquired data.

In some embodiments, the instructions for acquiring and processing datafor the one or more processes comprises instructions for reformattingdata types present in the run data store.

In some embodiments, the instructions for acquiring and processing datafor the one or more processes comprises instructions for changing astorage medium or a storage format used by the run data store.

In some embodiments, the instructions for acquiring and processing datafor the one or more processes comprises instructions for storing theacquired data.

In some embodiments, the instructions for acquiring and processing datafor the one or more processes comprises instructions for performing ananalysis of the one or more processes using the acquired data.

In some embodiments, the instructions for performing the analysiscomprises root cause analysis, correlation analysis, or a featureselection technique.

In some embodiments, the instructions for acquiring and processing datafor the one or more processes comprises instructions for initiating analert when a specific condition arises in a process in the one or moreprocesses.

In some embodiments, the instructions for acquiring and processing datafor the one or more processes comprises instructions for initiatinginstructions on the first device responsive to the acquired data.

In some embodiments, the one or more processes comprises a plurality ofprocesses. In some embodiments, the one or more processes comprises asingle process.

In some embodiments, the one or more versions comprises a plurality ofversions. In some embodiments, the one or more versions comprises asingle version.

In some embodiments, an input property in the one or more inputproperties associated with a parameterized resource input in the set ofparameterized resource inputs to the respective stage in the respectiveprocess includes an input specification limit.

In some embodiments, an output property in the one or more outputproperties associated with a parameterized resource output in the set ofparameterized resource outputs to the respective stage in the respectiveprocess includes an output specification limit.

In some embodiments, at least one parameterized resource input in theset of parameterized resource inputs is associated with one or moreinput properties, including an input specification limit.

In some embodiments, the one or more output properties consists of asingle output property and the single output property is an identifier.

In some embodiments, a first version and a second version in arespective plurality of versions for a process in the one or moreprocesses differ from each other in a number of nodes, a process stagelabel of a node, a parameterized resource input in a set ofparameterized resource inputs, or a parameterized resource output in aset of parameterized resource outputs.

In some embodiments, the set of parameterized resource inputs for a nodein the plurality of nodes of a hypergraph for a process version in therespective plurality of process versions comprises a first and secondparameterized resource input. The first parameterized resource inputspecifies a first resource and is associated with a first input property(e.g., a viscosity value, a purity value, composition value, atemperature value, a weight value, a mass value, a volume value, or abatch identifier of the first resource). The second parameterizedresource input specifies a second resource and is associated with asecond input property, where the first input property is different fromthe second input property. In some such embodiments, the first resourceis a single resource or a composite resource.

In some embodiments, the set of parameterized resource inputs for afirst node in the plurality of nodes of a hypergraph of a processversion in the respective plurality of process versions comprises afirst parameterized resource input. The first parameterized resourceinput specifies a process condition (e.g., a temperature, an exposuretime, a mixing time, a type of equipment, or a batch identifier)associated with the corresponding stage of the process associated withthe first node.

In some embodiments, the non-transitory computer readable storage mediumfurther stores instructions for maintaining one or more interfaces,where each respective interface in the one or more interfaces acquiresdata for the run data store from one or more corresponding instruments.In some such embodiments, a respective interface in the one or moreinterfaces directs a corresponding instrument to acquire data for therun data store. In some such embodiments, a respective interface in theone or more interfaces directs a corresponding instrument to acquirevalues of input or output properties. In some such embodiments, arespective interface in the one or more interfaces acquires data for therun data store from one or more corresponding instruments across anetwork connection.

In some embodiments, the non-transitory computer readable storage mediumfurther stores instructions for maintaining one or more interfaces foreffecting process control, where each respective interface in the one ormore interfaces controls one or more corresponding instrumentsassociated with a process in the one or more processes. In some suchembodiments, a first interface in the one or more interfaces controls afirst instrument through the specification of a process conditionassociated with the corresponding stage of the corresponding process.

In some embodiments, the first device is a single computer system, aplurality of networked computer systems, or a virtual machine.

In some embodiments, a node in the plurality of nodes is not associatedwith a set of parameterized resource inputs. In some embodiments, a nodein the plurality of nodes is not associated with a set of parameterizedresource outputs.

In some embodiments, two or more nodes in the plurality of nodes areeach associated with a corresponding set of parameterized resourceinputs. In some embodiments, two or more nodes in the plurality of nodesare each associated with a corresponding set of parameterized resourceoutputs.

Another aspect of the present disclosure provides a computer systemcomprising one or more processors, memory, and one or more programsstored in the memory for execution by the one or more processors. Theone or more programs comprise instructions for maintaining a hypergraphdata store, maintaining a run data store, and executing instructions foracquiring and processing data for the one or more processes.

In this aspect, the hypergraph data store comprises, for each respectiveprocess in a set of one or more processes, a respective plurality ofversions of the respective process. Each process in the one or moreprocesses results in a respective product or analytical information.Each respective version comprises a hypergraph comprising a plurality ofnodes connected by edges in a plurality of edges. Each respective nodein the plurality of nodes represents a respective stage in therespective process and is associated with a set of parameterizedresource inputs to the respective stage in the corresponding process,and/or a set of parameterized resource outputs to the respective stagein the corresponding process. Each respective edge in the plurality ofedges specifies that the set of parameterized resource outputs of a nodein the plurality of nodes is included in the set of parameterizedresource inputs of at least one other node in the plurality of nodes.

In this aspect, the run data store comprises a plurality of processruns. Each process run comprises an identification of a version in theplurality of versions for a process in the one or more processes. Eachprocess run further comprises values for the respective set ofparameterized resource inputs of a first node in the hypergraph of therespective version. Each process run further comprises the respectiveset of parameterized resource outputs of the first node in thehypergraph of the respective version. Each process run further comprisesobtained values of at least one output property of a parameterizedresource output in the respective set of parameterized resource outputsof the first node in the hypergraph of the respective version.

In some alternative embodiments, the computer system is in the form of asingle computer system, a plurality of networked computer systems, or avirtual machine.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system topology in accordance with the presentdisclosure that includes a device, namely a computer system 200, and aplurality of stages 20 of a process.

FIG. 2 illustrates a computer system in accordance with an embodiment ofthe present disclosure.

FIG. 3 illustrates a process version in accordance with an embodiment ofthe present disclosure.

FIG. 4 illustrates a run data store in accordance with an embodiment ofthe present disclosure.

FIG. 5 illustrates a process evaluation module in accordance with anembodiment of the present disclosure.

FIGS. 6A, 6B, 6C, 6D, and 6E collectively illustrate a flow chartproviding process design and analysis of one or more processes in whichsteps (nodes) are connected by resources (edges) in accordance with anembodiment of the present disclosure.

FIG. 7 illustrates a hypergraph comprising a plurality of nodesconnected by edges in which a fermenter setup stage is highlighted inaccordance with an embodiment of the present disclosure.

FIG. 8 illustrates the hypergraph of FIG. 7 in which a grow inoculumstage is highlighted in accordance with an embodiment of the presentdisclosure.

FIG. 9 illustrates the hypergraph of FIG. 7 in which a inoculatefermenter stage is highlighted in accordance with an embodiment of thepresent disclosure.

FIG. 10 illustrates the hypergraph of FIG. 7 in which a fed-batchfermentation stage is highlighted in accordance with an embodiment ofthe present disclosure.

FIG. 11 illustrates the hypergraph of FIG. 7 in which a new stage isbeing added to the hypergraph of FIG. 7 in accordance with an embodimentof the present disclosure.

FIG. 12 illustrates the hypergraph of FIG. 11 in which a DW Assay stageand Off-Gas Assay stage are added to the hypergraph of FIG. 7 inaccordance with an embodiment of the present disclosure.

FIG. 13 illustrates the hypergraph of FIG. 12 in which a new group ofstages is added to the hypergraph of FIG. 7 in accordance with anembodiment of the present disclosure.

FIG. 14 illustrates the hypergraph of FIG. 13 in which the new group ofstages is defined in accordance with an embodiment of the presentdisclosure.

FIG. 15 illustrates how the new group of stages defined in thehypergraph of FIGS. 13 and 14 is defined in accordance with anembodiment of the present disclosure.

FIG. 16 illustrates how the new standards prep stage in the new group ofstages defined in the hypergraph of FIGS. 13 and 14 is defined inaccordance with an embodiment of the present disclosure.

FIG. 17 illustrates how the new instrument calibration stage in the newgroup of stages defined in the hypergraph of FIGS. 13 and 14 is definedin accordance with an embodiment of the present disclosure.

FIG. 18 further illustrates how the new instrument calibration stage inthe new group of stages defined in the hypergraph of FIGS. 13 and 14 isdefined in accordance with an embodiment of the present disclosure.

FIG. 19 illustrates how the new run samples stage in the new group ofstages defined in the hypergraph of FIGS. 13 and 14 is defined inaccordance with an embodiment of the present disclosure.

FIG. 20 illustrates setting up process runs using the new group ofstages defined in the hypergraph of FIGS. 13 and 14 in accordance withan embodiment of the present disclosure.

FIG. 21 further illustrates setting up process runs using the new groupof stages defined in the hypergraph of FIGS. 13 and 14 in accordancewith an embodiment of the present disclosure.

FIG. 22 further illustrates setting up process runs using the new groupof stages defined in the hypergraph of FIGS. 13 and 14 in accordancewith an embodiment of the present disclosure.

FIG. 23 further illustrates the raw data for three different processruns of the new run samples stage in the new group of stages defined inthe hypergraph of FIGS. 13 and 14 in accordance with an embodiment ofthe present disclosure.

FIG. 24 further illustrates selecting to analyze the data illustrated inFIG. 23 in accordance with an embodiment of the present disclosure.

FIG. 25 illustrates analysis of the data illustrated in FIG. 23 inaccordance with an embodiment of the present disclosure.

FIG. 26 illustrates further analysis of the data illustrated in FIG. 23in accordance with an embodiment of the present disclosure.

FIGS. 27A, 27B, 27C, 27D, and 27E collectively illustrate a flow chartproviding process design and analysis of one or more processes in whichsteps (nodes) are connected by generic connectors (edges) with resourcelists associated with those edges in accordance with another embodimentof the present disclosure.

FIGS. 28A, 28B, 28C, 28D, and 28E collectively illustrate a flow chartproviding process design and analysis of one or more processes in whichsteps (nodes) are connected by generic connectors (edges) with noassociated lists in accordance with another embodiment of the presentdisclosure.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. However, it will beapparent to one of ordinary skill in the art that the present disclosuremay be practiced without these specific details. In other instances,well-known methods, procedures, components, circuits, and networks havenot been described in detail so as not to unnecessarily obscure aspectsof the embodiments.

It will also be understood that, although the terms first, second, etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first subject could be termed asecond subject, and, similarly, a second subject could be termed a firstsubject, without departing from the scope of the present disclosure. Thefirst subject and the second subject are both subjects, but they are notthe same subject.

The terminology used in the present disclosure is for the purpose ofdescribing particular embodiments only and is not intended to belimiting of the invention. As used in the description of the inventionand the appended claims, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will also be understood that the term “and/or”as used herein refers to and encompasses any and all possiblecombinations of one or more of the associated listed items. It will befurther understood that the terms “comprises” and/or “comprising,” whenused in this specification, specify the presence of stated features,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in response to detecting,” dependingon the context. Similarly, the phrase “if it is determined” or “if [astated condition or event] is detected” may be construed to mean “upondetermining” or “in response to determining” or “upon detecting [thestated condition or event]” or “in response to detecting [the statedcondition or event],” depending on the context.

A detailed description of a system 48 for providing process design andanalysis of one or more processes in accordance with the presentdisclosure is described in conjunction with FIGS. 1 through 5. Inparticular, FIG. 1 illustrates a process or pipeline having a pluralityof stages 20. Each respective stage 20 in FIG. 1 is illustrated by anexemplary reaction chamber to indicate that a form of materialtransformation takes place. However, there is no requirement that thismaterial transformation take place in a reaction chamber. In theembodiment illustrated in FIG. 1, each stage 20 includes a set ofparameterized inputs 308 and a set of parameterized outputs 314. Moregenerally, in some embodiments, each respective stage in the pluralityof stages comprises is associated with one or more inputs and at leastone output.

In some embodiments, as illustrated in FIG. 1, a description of theseinputs 308 and outputs 314 is provided to computer system 200, possiblyover communications network 106. For instance, at stage 20-2, when aprocess run completes this stage, a file that includes the parameterizedoutputs of this stage is stored in a directory associated with thisstage. Then, a sweeping or monitoring process takes this new file andsends it to computer system 200 where it is uploaded into acorresponding process run stored in the computer system 200. In moredetail, in some embodiments, inputs 308 or outputs 314 areelectronically measured by measuring devices. For instance, in someembodiments a software component such as a sync engine that runs as abackground process (like Google Drive or Dropbox Sync) on any computerattached to an instrument or other component of a stage 20 monitors asynced folder. When new instrument data files are added to the folder,the software parses and sends the data associated with the stage acrosscommunications network 106 to computer system 200. In some embodiments,a hardware solution is used to communicate the set of inputs 308 andoutputs 314 of the stages 20 of a process. In such an approach dataacquisition and transfer is performed by direct interface withinstruments or other components of stages 20. For instance, in someembodiments a BeagleBone black microcontroller(http://beagleboard.org/BLACK) is used to transmit such data to computersystem 200 across network 106. In some embodiments, data (e.g., valuesfor a set of parameterized resource inputs 310 and/or values for a setof parameterized resource outputs 314 associated with a stage 20 of aprocess) is communicated from the respective stages 20 to the computersystem via HTTPS port 443 via HTTP POSTs or representational statetransfer.

Of course, other topologies of system 48 are possible, for instance,computer system 200 can in fact constitute several computers that arelinked together in a network or be a virtual machine in a cloudcomputing context. As such, the exemplary topology shown in FIG. 1merely serves to describe the features of an embodiment of the presentdisclosure in a manner that will be readily understood to one of skillin the art.

Referring to FIG. 2, in typical embodiments, a computer system 200 forproviding process design and analysis of one or more processes comprisesone or more computers. For purposes of illustration in FIG. 2, thecomputer system 200 is represented as a single computer that includesall of the functionality of the computer system 200. However, thedisclosure is not so limited. The functionality of the computer system200 may be spread across any number of networked computers and/or resideon each of several networked computers and/or by hosted on one or morevirtual machines at a remote location accessible across thecommunications network 106. One of skill in the art will appreciate thata wide array of different computer topologies is possible for thecomputer system 200 and all such topologies are within the scope of thepresent disclosure.

The computer system 200 is uniquely structured to record and store datain a computable way with minimal effort, quantitatively search allexperimental designs, and data, or any subset thereof, apply real-timestatistical analysis, achieve quality by design, update experimentalprocesses and data collection systems, identify meaningful variables viaautomated critical-to-quality analysis, routinely obtain results thatare true and unequivocal, access transparent data and results, makeresults open and accessible (and securely control access to anyone orany team), build quantitatively and directly on others' designs andresults, and unambiguously communicate evidence supporting a conclusionto team members or partners.

Turning to FIG. 2 with the foregoing in mind, a computer system 200comprises one or more processing units (CPU's) 274, a network or othercommunications interface 284, a memory 192 (e.g., random access memory),one or more magnetic disk storage and/or persistent devices 290optionally accessed by one or more controllers 288, one or morecommunication busses 112 for interconnecting the aforementionedcomponents, and a power supply 276 for powering the aforementionedcomponents. Data in memory 192 can be seamlessly shared withnon-volatile memory 290 using known computing techniques such ascaching. Memory 192 and/or memory 290 can include mass storage that isremotely located with respect to the central processing unit(s) 274. Inother words, some data stored in memory 192 and/or memory 290 may infact be hosted on computers that are external to computer system 200 butthat can be electronically accessed by the computer system over anInternet, intranet, or other form of network or electronic cable(illustrated as element 106 in FIG. 2) using network interface 284.

The memory 192 of computer system 200 stores:

-   -   an operating system 202 that includes procedures for handling        various basic system services;    -   a hypergraph data store 204 store comprising, for each        respective process 206 in the one or more processes, a        respective plurality of versions 208 of the respective process        206;    -   a run data store 206 that stores a plurality of process runs,        each process run comprising an identification of a version 208        in the plurality of versions for a process in the one or more        processes;    -   a statistics module 212 for analyzing the process data;    -   a process evaluation module 216 for initiating alerts when        specific conditions arise in a process; and    -   one or more optional data drivers 218, each data driver for a        respective process in the one or more processes, the data driver        including instructions for receiving a dataset for the        respective process and instructions for processing the dataset.

In some implementations, one or more of the above identified dataelements or modules of the computer system 200 are stored in one or moreof the previously mentioned memory devices, and correspond to a set ofinstructions for performing a function described above. The aboveidentified data, modules or programs (e.g., sets of instructions) neednot be implemented as separate software programs, procedures or modules,and thus various subsets of these modules may be combined or otherwisere-arranged in various implementations. In some implementations, thememory 192 and/or 290 optionally stores a subset of the modules and datastructures identified above. Furthermore, in some embodiments the memory192 and/or 206 stores additional modules and data structures notdescribed above.

Turning to FIG. 3, more details of embodiments of a process version 208are described. The process version comprises a hypergraph 302. Thehypergraph 302 comprises a plurality of nodes, is directional, causal,and sequential based. For instance, each respective node 304 in theplurality of nodes is connected to at least one other node in theplurality of nodes by an edge. Each respective node 304 in the pluralityof nodes comprises a process stage label 306 representing a respectivestage in the corresponding process. In some embodiments, a node 304 is acomplete and self-contained description of a transformative event thatcan be used to build larger processes. A node 304 is sufficientlygeneral to serve in a wide array of processes, such as chemicalprocesses, life science processes, and food preparation process.Advantageously, nodes 304 do not lose their meaning or utility whencopied into other process. As such, the definition of a particular node304 does not depend on the definition of other another in a hypergraph302 in preferred embodiments. As illustrated in FIG. 3, nodes 304 arestructured to contain data in a unique way, in order to facilitatesubsequent data mining and reasoning engines to analyze process runsbased on process versions 208.

In some embodiments, each respective node 304 in the plurality of nodesis associated with a set of parameterized resource inputs 308 to therespective stage in the corresponding process. At least oneparameterized resource input 310 in the set of parameterized resourceinputs 308 is associated with one or more input properties 312, the oneor more input properties including an input specification limit 314.Examples of input properties 312 are the attributes (e.g., measurements,quantities, etc.) of things such as people, equipment, materials, anddata. There can be multiple input properties for a single parameterizedresource input (e.g., temperature, flow rate, viscosity, pH, purity,etc.). In some embodiments, there is a single input property for aparticular parameterized resource input. In such embodiments, eachrespective node 304 in the plurality of nodes is also associated with aset of parameterized resource outputs 314 to the respective stage in thecorresponding process. At least one parameterized resource output 316 inthe set of parameterized resource outputs 314 is associated with one ormore output properties 318, the one or more output properties includinga corresponding output specification limit 320. Examples of outputproperties 318 include attributes (e.g., measurements, quantities, etc.)of things such as people, equipment, materials, and data. There can bemultiple output properties for a single parameterized resource output.In some embodiments, there is a single output property for a particularparameterized resource output.

FIGS. 17 and 18 illustrate the above concepts. FIG. 17 illustrates aportion of a hypergraph 302 and illustrates the nodes 304 in the portionof the hypergraph. The node “Instrument Calibration” 304-14 ishighlighted. Accordingly, the set of parameterized resource inputs 308and the set of parameterized resource outputs 314 for this node areshown on the right side of FIG. 17. The set of parameterized resourceinputs 308 for the node “Instrument Calibration” includes sulfuric acid310-1, column 310-2, citric acid 310-3, glucose 310-4, glycerol 310-5,and HPLC 310-6. As such, the exemplary set of parameterized resourceinputs 308 illustrates that two of the many possible types ofparameterized inputs are (i) compositions (e.g., sulfuric acid, citricacid, glucose, glycerol, etc.) and (ii) type of equipment (e.g., column,HLLC, etc.). The set of parameterized resource outputs 314 for the node“Instrument Calibration” consists of HPLC 316.

Turning to FIG. 18, more details regarding the parameterized resourceinput 310-2 “column” and parameterized resource input 310-3 “citricacid” are provided. The parameterized resource input 310-3 “citric acid”is associated with one or more input properties 312 including an inputspecification limit 314. For instance, one input property forparameterized resource input 310-3 “citric acid” is “pH” 312-3-1 andthis property includes an input specification limit 314-3-1. In fact,the input specification limit 314-3-1 is expressed as a lower limit (pH3.5), a target limit (pH 4), and an upper limit (pH 4.5). Another inputproperty for parameterized resource input 310-3 “citric acid” is“concentration” 312-3-2 and this property includes an inputspecification limit 314-3-2. The input specification limit 314-3-2 isexpressed as a lower limit (9.75 g/L Units), a target limit (10 g/LUnits), and an upper limit (10.25 g/L Units).

Returning to FIG. 3, each hypergraph 302 includes a plurality of edges.Each respective edge 322 in the plurality of edges specifies that theset of parameterized resource outputs 314 of a source node 304 in theplurality of nodes is included in the set of parameterized resourceinputs 308 of at least one other destination node 304 in the pluralityof nodes. In other words, an edge specifies that the state of amaterial, equipment, people or other thing inputted into one node(destination node) in a given process is identical to the state ofmaterial, equipment, people, or other thing that has been outputted fromanother node (source node) in the hypergraph for that process. In someembodiments, an edge specifies that the state of a material, equipment,people or other thing inputted into a plurality of nodes (destinationnode) is identical in a given process to the state of material,equipment, people, or other thing that has been outputted from anothernode (source node) in the hypergraph for that process. Moreover, adestination node may be connected to two or more source nodes meaningthat the input the destination node includes material, equipment, peopleor other thing in the same state as it was in the output of the two ormore source nodes for a given process.

Process versioning 208 is an advantageous feature of the disclosedsystems and methods. For example, when the input or output of aparticular node is identified through correlation analysis acrossvarious process runs of a process to be a cause of poor reproducibilityof the overall process, additional nodes before and after theproblematic node can be added in successive versions of the process andprocess runs of these new versions of the process can then be executed.Moreover, advantageously, data from older versions and newer versions ofthe process can be used together in correlation analysis, in someembodiments, across all the process runs of all of the process versionsto determine the root cause of the variability or other unfavorableattribute associated with the problematic node and hereby develop aprocess version that adequately addresses the problem. In fact, processruns from multiple processes that make similar but not identicalproducts or produce similar but not identical analytical information canbe analyzed to identify such problems.

As FIG. 3 illustrates, each node 304 has inputs (set of parameterizedresource inputs 308), and each of these parameterized resource inputs310 has one or more input properties 312, and each these inputproperties has input specification limits 314. Further, each node 304has one or more parameterized resource outputs (set of parameterizedresource outputs 314), and each of these parameterized resource outputs316 has one or more output properties 318. Moreover, each of theseoutput properties has output specification limits 320. The set ofparameterized resource outputs serve as the inputs to other nodes andsuch relationships are denoted by edges. Moreover, the set ofparameterized resource outputs 314 of a particular node can serve as theinputs to more than one node, thus the edges and nodes constitute ahypergraph. By defining a process in this way, it is easy to createprocess versions 208, integrate data acquisition from disparate sourcesand devices, and query process runs to identify correlations, reduceexperimental variance, and improve process reproducibility. Process runsinvoke a process version and result in values (e.g. measurements) forthe set of inputs and set of outputs of a node in the hypergraph in theprocess version.

In some instances, a destination node 304 includes only a single edge322 from one source node 324. In such instances, the set ofparameterized resource outputs 314 for the source node 324 constitutesthe entire set of parameterized resource inputs 308 for the destinationnode 326. This is illustrated in FIG. 17 where there is a single edge322-14 between node 304-14 and 304-15. Thus, the set of parameterizedresource outputs 314 for node 304-14 constitute the entire set ofparameterized resource inputs 308 for node 304-15.

To illustrate the concept of a node in a process, consider a node thatis designed to measure the temperature of fermenter broth. The set ofparameterized inputs 308 to this node include a description of thefermenter broth and the thermocouple that makes the temperaturemeasurement. The thermocouple will include input properties that includeits cleanliness state, calibration state and other properties of thethermocouple. The set of parameterized outputs 314 to this node 304include the temperature of the fermenter broth, and output specificationlimits for this temperature (e.g., an acceptable range for thetemperature). Another possible parameterized resource output 316 of thenode 304 is the thermocouple itself along with properties 316 of thethermocouple after the temperature has been taken, such as itscleanliness state and calibration state. For each of these properties316 there is again corresponding output specification limits.

In some instances, a destination node 304 includes multiple edges 322,each such edge from a different source node 324. In such instances, theset of parameterized resource outputs 314 for each such source node 324collectively constitute the set of parameterized resource inputs 308 forthe destination node 326. This is illustrated in FIG. 17 where there isa first edge (edge 322-11) between source node 304-13 and destinationnode 304-14 and a second edge (edge 322-12) between source node 304-12and destination node 304-14. Thus, the set of parameterized resourceoutputs 314 for node 304-13 plus the set of parameterized resourceoutputs 314 for node 304-12 constitute the set of parameterized resourceinputs 308 for node 304-14.

Turning to FIG. 4, more details of a run data store 210 are provided.The run data store 210 comprises a plurality of process runs. That is tosay, when a node of a process is run, actual material lots or pieces ofequipment, etc., are obtained and/or used as real world instances of aparticular process version 406. As such, each process run 402 comprisesan identification of a node 406 of an identified 404 process version 208in the plurality of versions for a process 206 in the one or moreprocesses. For the identified node 406 of the process version 208, theprocess run 402 further comprises values for the respective set ofparameterized resource inputs 308 of a first node 304 in the hypergraph302 of the respective process version 208 and their associated inputproperties 312. For the identified process version 208, the process run402 also comprises the respective set of parameterized resource outputs314 of the first node 304. Further, for the identified process version208, the process run 402 also comprises obtained values of at least oneoutput property 318 of a parameterized resource output 316 in therespective set of parameterized resource outputs 314 of the first node304 in the hypergraph 302 of the respective process version. FIGS. 19through 24 illustrate three process runs 402 for a particular processversion, with each process run 402 characterized by different conditions(e.g., different amounts for one or more input properties of one or moreparameterized inputs to one or more nodes in the hypergraph of theprocess version).

In some embodiments, run data store 210 includes a genealogical graph420 comprising one or more process sets 422. Each process set 422comprises the identities 424 of related process versions 424. Forinstance, in some embodiments, a first process version 404 in a processset 420 and a second process version 404 in the process set 420 have thesame hypergraph but an output property, output specification limit,input property, or input specification limit to one of the nodes in thehypergraph is different. In another example, a first process version 404in a process set 420 and a second process version 404 in the process set420 have hypergraphs that have all but one, all but two, all but three,or all but four nodes in common. Typically, the process versions in aprocess set are related to each other in the sense that a process getsrefined over time, and various versions of the process are saved asprocess versions. Refinement of a process includes any combination ofadding or removing nodes from a hypergraph, adding or removing edgesfrom the hypergraph, adding or removing parameterized resource inputs toone or more nodes in the hypergraph, adding or removing parameterizedresource outputs to one or more nodes in the hypergraph, adding,removing or changing an input property or input specification limit of aparameterized resource input of one or more nodes in the hypergraph,and/or adding, removing or changing an output property or outputspecification limit of a parameterized resource output of one or morenodes in the hypergraph.

Turning to FIG. 5, more details of a process evaluation module 216 areprovided. The process evaluation module 216 takes advantage of theunique architecture of the disclosed processes. In particular, whenassessing whether a parameterized resource output 316 for a particularnode 304 satisfies a particular associated output specification limit320, it is only necessary to evaluate the values in process runs for thecorresponding parameterized resource output of that node. The inputs andoutputs of other nodes do not need to be evaluated for this purpose. Soit is possible to generate an alert in the form of a computer datatransmission when an obtained value for an output property of aparameterized resource output in a set of parameterized resource outputsfor a run of a node in a hypergraph for a process version is outside theoutput specification limit. Moreover, this alert is portable to otherprocess versions that make use of the same node. Thus, in someembodiments, a process evaluation module is organized by process version502. For each process version, nodes 504 for which process alerts areneeded are identified. For each such node 504, one or more parameterizedresource outputs 506 to the node are identified. For each of the one ormore parameterized resource outputs, one or more alert values 508 areidentified for the parameterized resource outputs. If an alert value istriggered for a property of a parameterized resource output of aparticular node of a particular process version, then a computer datatransmission 510 corresponding to the alert is communicated. In someembodiments, the computer data transmission 510 is a message sent to auser interface or a client computer indicating that the alert has beentriggered. In some embodiments, the computer data transmission is in theform of a text message, an E-mail, an SMS message, or an audible alarm.To illustrate, consider the case in which the output specification limit320 of an output property 318 of a parameterized resource output 316 ofa node 304 specifies that output pH should not exceed 7. Thus, an alert508 is set up for this output property. If, in a process run 502, the pHof the resource output does in fact exceed 7.0, than a computer datatransmission 510 corresponding to this alert is communicated.

System 48 provides a unique design for processes through unambiguousdefinition of state (e.g., the state of node inputs and node outputs) atwhatever level of resolution needed to achieve the performance goals ofa process (e.g., to satisfactorily stabilize the process). Such statesinclude, for example, the “what” and “how much” for each of the nodeinputs and outputs. Examples of “what” can be a piece of equipment,human resource, type material or composition of matter, to name a fewexamples. System 48 advantageously provides a way to unite multipledisparate functional areas (e.g., chemistry, biology, fermentation,analytical, different control systems, etc.) into a seamless process ofrepeatable material transformations (nodes) that can be versioned andfor which the data from process runs can be evaluated using statisticaltechniques to achieve product control (e.g., identify root causes ofunwanted variability).

Advantageously, the disclosed data structures fully define nodes (theirinput, their output, and hence the transformation that takes place ateach node) without any ambiguity in the pertinent properties of eachnode input and each node output. However, it is to be noted that theactual transformation that takes place within a node does notnecessarily need to be defined beyond a basic description (stage label)for record keeping and identification purposes. In some instances,process runs, in which the inputs of a node in a process are varied, arerun and the outputs or final product of the process is statisticallyanalyzed in view of these varied inputs to determine if the change inthe inputs improves an aspect of the final product of the process (e.g.,reproducibility, yield, etc.). One benefit of the disclosed systems andmethods is that they provide mechanisms to truly understand the dynamicsof a process (e.g., how variance in certain node inputs or properties ofnode inputs affect final product) and therefore allows the process to besuccessfully scaled up in size more easily. Because of the way processesare defined in the disclosed systems and methods, it is possible to findsources of error that cause undesirable results (e.g. bad yield, poorreproducibility, etc.) in defined processes, or for that matter,desirable results. Examples of unwanted error in processes isapplication dependent and depends, for example on the type of node inputor output, but can be for instance, measurement error or failure toquantify or even identify a relevant property of a node input or nodeoutput. For instance, if a node input is sugar, a measurement error mayarise because the process by which the weight of the sugar input to thenode is measured is not sufficiently accurate. In another example, if anode input is sugar, a relevant property of the sugar may be lot number,because in the particular process, sugar lot number happens to have aprofound impact on overall product yield.

Now that details of a system 48 for providing process design andanalysis of one or more processes have been disclosed, details regardinga flow chart of processes and features of the network, in accordancewith an embodiment of the present disclosure, are disclosed withreference to FIG. 6.

As illustrated in block 602 of FIG. 6A, a hypergraph data store 204 ismaintained. The hypergraph data store 204 comprises, for each respectiveprocess 206 of one or more processes, a respective plurality of versionsof the respective process. Each respective version 208 comprises ahypergraph 302 comprising a plurality of nodes 304 connected by edges322 in a plurality of edges. Each respective node 304 in the pluralityof nodes comprises a process stage label representing a respective stagein the corresponding process.

FIG. 7 illustrates a process version 208. The process version includes ahypergraph that includes a plurality of nodes 304 corresponding torespective stages of a process (e.g., “Fermenter Prep,” “FermenterSetup,” “Media Prep,” “Grow Inoculum,” “Innoculate Fermenter,”“Fed-Batch Fermentation,” and “Measure T, Ph, D, DO”). In someembodiments, concurrency is supported. That is, multiple users, eachoperating at a different client computer in communication with computersystem 200, can view an instance of the process version displayed inFIG. 7, make changes to it, and view and analyze data from process runsthat make use of it.

Each node 304 is associated with a set of parameterized resource inputs308 to the respective stage in the corresponding process. At least oneparameterized resource input 310 in the set of parameterized resourceinputs 308 is associated with one or more input properties 312. The oneor more input properties include an input specification limit 314. Eachnode 304 is also associated with a set of parameterized resource outputs314 to the respective stage in the corresponding process. At least oneparameterized resource output 316 in the set of parameterized resourceoutputs is associated with one or more output properties. The one ormore output properties include a corresponding output specificationlimit. FIG. 7 illustrates the set of parameterized resource inputs 308and the set of parameterized resource outputs 314 for the node 304-4“Fermenter Setup.” FIG. 8 illustrates the set of parameterized resourceinputs 308 and the set of parameterized resource outputs 314 for thenode 304-3 “Grow Inoculum.” FIG. 9 illustrates the set of parameterizedresource inputs 308 and the set of parameterized resource outputs 314for the node 304-5 “Inoculate Fermenter.” FIG. 10 illustrates the set ofparameterized resource inputs 308 and the set of parameterized resourceoutputs 314 for the node 304-6 “Fed-Batch Fermentation.” In someembodiments, a user can simply click on a node 304 to see their inputsand outputs. Moreover, unstructured data in the form of videos,pictures, or comments can be added to nodes 304. For example, a videoshowing the proper way to perform a procedure associated with a node canbe linked to a node by simply dragging an icon link to the video ontothe representation of node 304. For example, a video on the proper wayperform a fermenter setup can be dragged onto the “Fermenter Setup” node304-4 of FIG. 7. Thereafter, when a user clicks on node 304-4, the videois played.

Each respective edge 322 in the plurality of edges specifies that theset of parameterized resource outputs of a node in the plurality ofnodes is included in the set of parameterized resource inputs of atleast one other node in the plurality of nodes. Thus, turning to FIG. 7to illustrate, the set of parameterized resource inputs for node 304-6“Fed-Batch Fermentation” consists of the set of parameterized resourceoutputs for nodes 304-5 “Inoculate Fermenter” and 304-2 “Media Prep.”

FIGS. 11 and 12 illustrate adding new nodes 304-8 “DW Assay” and 304-9“Off-Gas Assay” to an existing hypergraph and FIGS. 13 and 14 illustrateadding a group of nodes entitled “HPLC Assay” to the hypergraph. TheHPLC Assay group is an extension of the existing hypergraph of FIG. 7and includes nodes and edges of this extension. Referring to FIGS. 15and 16, HPLC Assay begins with three initial nodes, node 304-10 “SolventPrep,” node 304-11 “Column Prep,” and node 304-12 “Standards Prep.” Insome embodiments, the names of nodes are chosen by a user from adatabase of allowed node names in order to ensure conformity in nodenames. In some embodiments, the names of node inputs 310 and outputs 316are also chosen by a user from a database of allowed node input andoutput names in order to ensure conformity in node input and outputnames. In some embodiments, the names of node input properties 312 andnode output properties 318 are also chosen by a user from a database ofallowed node input property names and node output property names inorder to ensure their conformity. FIG. 17 illustrates the portion of thehypergraph 302 encompassed by “HPLC Assay” after more nodes have beendefined in this portion of the hypergraph. The node “InstrumentCalibration” 304-14 is selected in FIG. 17. Accordingly, the set ofparameterized resource inputs 308 and the set of parameterized resourceoutputs 314 for node 304-14 are shown on the right side of FIG. 17.

As discussed above, versions 208 of a process 206 are related to eachother. In some embodiments, each version 208 of a process 604 producesthe same product. However, typically a first version and a secondversion in a respective plurality of versions for a process differ fromeach other in some way, such as in a number of nodes, a process stagelabel of a node, a parameterized resource input in a set ofparameterized resource inputs, a parameterized resource output in a setof parameterized resource outputs, a parameterized resource inputspecification limit, or a parameterized resource output specificationlimit, to name some possibilities (604).

To illustrate a set of parameterized resource inputs 308, in someembodiments, the set of parameterized resource inputs 308 for a node 304in the plurality of nodes of a hypergraph 302 for a process version 208in the respective plurality of process versions comprises a first 310-1and second parameterized resource input 310-2. The first parameterizedresource input specifies a first resource and is associated with a firstinput property 312-1 (606). The second parameterized resource input310-2 specifies a second resource and is associated with a second inputproperty 312-2. In some embodiments, the first input property is aviscosity value, a purity value, composition value, a temperature value,a weight value, a mass value, a volume value, or a batch identifier ofthe first resource (608). FIG. 7 illustrates. Node 304-4 “FermenterSetup” includes in its associated set of parameterized resource inputs308 a fermenter 310-3 and a waste bottle 310-5 among other resourceinputs. Although not shown in FIG. 7, the fermenter 310-3 is associatedwith a first input property, such as a size of the fermenter or afermenter make/model number. Furthermore, waste bottle 310-5 isassociated with a second input property, such as a size of the wastebottle 310-5 or a waste bottle 310-5 make and model number.

In some embodiments a resource input 310 is a single resource. Forinstance, in FIG. 7, resources 310-1 through 310-10 are all examples ofsingle resources. In some embodiments, a resource input 310 is acomposite resource. Examples of composite resources include, but are notlimited, to mixtures of compositions (e.g., media, broth, etc.) andmulti-component equipment.

Referring to FIG. 6B, in some embodiments, the set of parameterizedresource inputs 308 for a first node 304 in the plurality of nodes of ahypergraph 302 of a process version 208 in the respective plurality ofprocess versions comprises a first parameterized resource input 310 andthis first parameterized resource input specifies a process conditionassociated with the corresponding stage of the process associated withthe first node 304 (612). For example, in some embodiments, this processcondition is a temperature, an exposure time, a mixing time, aconcentration, a type of equipment or a batch identifier (614).

As noted above, for a given node, at least one of the parameterizedresource outputs in the set of parameterized resource outputs for thenode is associated with one or more output properties, and the one ormore output properties includes a corresponding output specificationlimit. In some embodiments, this corresponding output specificationlimit comprises an upper limit and a lower limit for the correspondingparameterized resource output (616). To illustrate, an example of anoutput property is pH of a composition. In such an example, the outputspecification limit specifies the allowed upper limit for the pH of thecomposition and the allowed lower limit for the pH of the composition.In alternative embodiments, this corresponding output specificationlimit comprises an enumerated list of allowable types (618). Toillustrate, an example of an output property is a crystallographicorientation of a material. In such an example, the output specificationlimit specifies an enumerated list of allowed crystallographicorientations for the material.

In some embodiments, the one or more processes in a hypergraph datastore is, in fact, a plurality of processes. Further, a first process inthe plurality of processes results in a first product and a secondprocess in the plurality of processes results in a different secondproduct (620). For instance, a first process in the hypergraph datastore may result in the manufacture of one type of composition andanother process in the hypergraph data store may result in themanufacture of another composition.

Referring to block 622, of FIG. 6B, a run data store 210 is alsomaintained. The run data store comprises a plurality of process runs402. In typical embodiments, a process version 208 is locked before aprocess run 402 for the process version 208 is executed so that nofurther changes can be made to the process version 208. If changes tounderlying process 206 are desired, a new process version 208 is definedin such embodiments.

Each process run 402 comprises an identification of a first node of aprocess version 404 (208) in the plurality of versions for a process 206in the one or more processes, as illustrated in FIG. 4. Furthermore,values for the respective set of parameterized resource inputs 408 ofthe first node 406 in the hypergraph 302 of the respective version andtheir associated input properties 410 are provided in a process run.More precisely, values for the properties of the parameterized resourceinputs in the set of parameterized resource inputs 408 of a node 406 inthe hypergraph 302 of the respective version are provided in a processrun. FIGS. 20 through 22 illustrate setting up three process runs,402-1, 402-2, and 402-3 for a particular node of a process version basedupon the hypergraph illustrated in FIG. 19. In FIG. 22, values forproperties of the parameterized resource inputs “Citric Acid” and“Column” are entered. In particular, referring to FIG. 22, for theproperty “pH” of parameterized resource input “Citric Acid” is set to 5and the value for the property “packing material” for the parameterizedresource input “Column” is set to “Saphadex HR.” FIG. 23 shows raw datafrom such process runs. FIG. 24 shows selecting to analyze these processruns and FIG. 25 shows the resulting analysis of such process runs.Conveniently, as illustrated in FIGS. 25 and 26, query 2502 and toggles2504 can be used to select which properties (e.g., input or outputproperties of the nodes of the underlying process versions) of whichprocess runs are viewed. Furthermore, referring to FIG. 26, calculatedproperties (e.g., amount of final product divided a quantity of inputmaterial) based upon the raw data from such process runs as well ascorrelations between calculated properties can be viewed.Advantageously, because of the structured way in which process runs aredefined based on a node of underlying process versions, it is possibleto automatically set up predefined process calculations (e.g. a processyield calculation) of the raw data (e.g., the raw data illustrated inFIG. 23) of executed process runs so that when new process runs areperformed such process calculations are automatically applied to the rawdata. This greatly reduces the labor in analyzing process runs.

Each process run 402 comprises the respective set of parameterizedresource outputs 412 of the subject node 304 in the hypergraph 302 ofthe respective version 208. The process run 402 further comprisesobtained values of at least one output property of a parameterizedresource output in the respective set of parameterized resource outputsof the node.

In some embodiments, the run data store 210 further comprises agenealogical graph 420 showing a relationship between (i) versions of asingle process in the plurality of versions of a process that are in theplurality of process runs or (ii) versions of two or more processes inthe respective plurality of versions of two or more processes that arein the plurality of process runs (624). For instance, in someembodiments, a first process version 404 in a process set 420 and asecond process version 404 in the process set 420 have the samehypergraph but an output property, output specification limit, inputproperty, or input specification limit to one of the nodes in thehypergraph is different. In another example, a first process version 404in a process set 420 and a second process version 404 in the process set420 have hypergraphs that have all but one, all but two, all but three,or all but four nodes, and so forth, in common. The genealogical graphprovides an advantageous way of discerning the relationship between thevarious process versions of a given process.

Turning to FIG. 6C, a statistics module 212 is also maintained (626).The statistics module 212 leverages the structure of run data store 210and hypergraph data store 204 to enable analytics of process runs. Inparticular, the statistics module 212 combined with the unique structureof run data store 210 and hypergraph data store 204 provides anadvantageous platform for supporting statistical process control (SPC)over the many disparate components of a process 206 and thus providespowerful tools for analyzing and stabilizing such processes. SPC is amethod of quality control which uses statistical methods. It is appliedin order to monitor and control processes. Monitoring and controllingprocesses ensures that they operate at their full potential. Forinstance, at its full potential, a process 206 can make as muchconforming product as possible with a minimization of waste. SPC can beapplied to any process 206 where the “conforming product” (productmeeting specifications) output can be measured. SPC makes use of controlcharts, a focus on continuous improvement and the design of process runs402 (e.g., experiments). See, for example, Barlow and Irony, 1992,“Foundations of statistical quality control” in Ghosh, M. & Pathak, P.K. (eds.) Current Issues in Statistical Inference: Essays in Honor of D.Basu, Hayward, Calif., Institute of Mathematical Statistics, pp. 99-112,which is hereby incorporated by reference.

Advantageously, rather than having to track down the disparate data indisparate forms associated with a process or, rather the process runsthat make use of the nodes of the process, in order to support SPC, thestatistics module 212, responsive to receiving a query that identifiesone or more first parameterized resource inputs and/or parameterizedresource outputs present in one or more process runs in the run datastore, is able to easily retrieve and format the one or more firstparameterized resource inputs and/or parameterized resource outputs foranalysis. In some embodiments, for example, the data is formatted as oneor more tab delimited files, CSV files, EXCEL spreadsheets, GOOGLESheets, and/or in a form suitable for relational databases. Inparticular, the data is structured to ensure that such data can beefficiently analyzed so that potential correlations are not overlookedin subsequent analysis. An example of such analysis that is performed aspart of SPC is correlation analysis such as the root cause analysisillustrated in FIG. 26. Root cause analysis is described, for example,in Wilson et al., 1993, Root Cause Analysis: A Tool for Total QualityManagement, Milwaukee, Wis., ASQ Quality Press. pp. 8-17, which ishereby incorporated by reference. Leading up to the root cause analysisillustrated in FIG. 26, a query identifies one or more firstparameterized resource inputs (e.g., amino acid type) and/orparameterized resource outputs (e.g., citrate concentration) present inone or more process runs in the run data store. Data for the one or morefirst parameterized resource inputs and/or parameterized resource isthen formatted and outputted for analysis. With this formatted data, ananalysis, such as the root cause analysis of FIG. 26, is conducted. Insome embodiments, the query results are formatted for a third partystatistical analysis package such as IMP (SAS, Buckinghamshire, England,http://www.jmp.com/en_dk/software.html). Analysis using such a thirdparty statistical analysis package typically results in proposals fornew process versions, in which nodes are added or removed, or the inputsor outputs to existing nodes are further defined or redefined, in orderto identify and remove unwanted process variability (e.g., to stabilizethe process).

The query can be of any of the resource inputs or outputs available forany combination of process versions of any combination of the one ormore processes in the run data store 210 or properties of these inputsor outputs. As such, in some embodiments, the query further identifiesone or more second parameterized resource inputs and/or parameterizedresource outputs present in one or more runs in the run data store (orproperties thereof) and the one or more first parameterized resourceinputs and/or parameterized resource outputs and the one or more secondparameterized resource inputs and/or parameterized resource outputs arecorrelated and a numerical measure of this correlation is formatted forpresentation (628). In some embodiments, the numerical measure ofcorrelation is on a scale between a low number and a high number, wherethe low number (e.g., zero) is indicative of no correlation and the highnumber (e.g., one) is indicative of complete correlation across the oneor more first parameterized resource inputs and/or parameterizedresource outputs and the one or more second parameterized resourceinputs and/or parameterized resource outputs.

In some embodiments, the query further identifies one or more secondparameterized inputs and/or parameterized outputs present (or theirproperties) in one or more runs in the run data store, and thestatistics module further identifies a correlation between (i) the oneor more first parameterized inputs and/or parameterized outputs and (ii)the one or more second parameterized inputs and/or parameterized outputspresent in one or more process runs in the run data store from among allthe parameterized inputs and/or parameterized outputs present in the rundata store using a multivariate analysis technique (630).

In some embodiments, the query identifies (i) one or more properties ofone or more first parameterized inputs and/or parameterized outputs and(ii) one or more properties of one or more second parameterized inputsand/or parameterized outputs present in one or more runs in the run datastore, and the statistics module further seeks a correlation between (i)the identified properties of the one or more first parameterized inputsand/or parameterized outputs and (ii) the identified one or moreproperties of the one or more second parameterized inputs and/orparameterized outputs present in one or more process runs in the rundata store from among all the parameterized inputs and/or parameterizedoutputs present in the run data store using a multivariate analysistechnique.

In some embodiments, the above processes invoke a multivariate analysistechnique that comprises a feature selection technique (632) (e.g.,least angle regression, stepwise regression). Feature selectiontechniques are particularly advantageous in identifying, from among themultitude of variables (e.g., values for input properties of inputs andvalues for output properties of outputs of nodes) present across sets ofprocess runs, which variables (e.g., which input properties of inputs ofwhich nodes and/or which output properties of outputs of which nodes)have a significant causal effect on a property of the product of theprocess (e.g., which of the variables are causal for poorreproducibility, poor yield, or conversely which of the variables arecausal for excellent reproducibility, higher yield). Feature selectiontechniques are described, for example, in Saeys et al., 2007, “A reviewof feature selection techniques in bioinformatics,” Bioinformatics 23,2507-2517, and Tibshirani, 1996, “Regression and Shrinkage and Selectionvia the Lasso,” J. R. Statist. Soc B, pp. 267-288, each of which ishereby incorporated by reference.

In some embodiments, the one or more processes are a plurality ofprocesses and the correlation is identified from process runs in asubset of the plurality of processes (634). There is no requirement thateach of the processes across which this correlation is identified makethe same product in such embodiments. Such embodiments are highlyadvantageous because they allow for the investigation of undesirableprocess variability across process runs used in the manufacture ofdifferent products. For instance, some of the process runs used in acorrelation analysis may manufacture biologic A and other process runsused in the same correlation analysis may manufacture biologic B.Correlation analysis that uses data from process runs for biologics Aand B allows for the investigation of causes of variation that areproduct independent, such as, for example, a poorly defined fermentationstep. For example, the sugar input into this fermentation step in theprocess runs for both biologics A and B may not be adequately defined toensure process stabilization. Another example of a source of variationcommon to these process versions could be, for example, identifiedthrough correlation analysis across process runs for both biologics Aand B, to a piece of equipment that is beginning to fail due to age.This is all possible because the disclosed systems and methodsadvantageously impose a consistent framework to the process runs thatmake different products. Thus, it is possible to aggregate process runsfrom across different products and perform cross-sectional filtering onany desirable set of inputs, input properties, outputs, and/or outputproperties, or specification limits thereof in these process runs, inorder to, for example, discover sources of process variability that areindependent (or dependent) of actual products made by such processes.

In some embodiments, the one or more processes are a plurality ofprocesses and the correlation is identified from process runs in asingle process in the plurality of processes (636). In such embodiments,each of the processes across which this correlation is identified makesthe same product or produce the same analytical information. Suchembodiments are used, for example, to precisely identify key sources ofvariability in the manufacture of the product or production of theanalytical information through the process.

In some embodiments, the one or more processes is a plurality ofprocesses and the query further identifies a subset of the plurality ofprocesses whose process runs are to be formatted by the statisticsmodule (638).

Turning to FIG. 6D, in some embodiments the statistics module 212further provides suggested values for the one or more secondparameterized inputs for one or more additional process runs of a firstprocess in the one or more processes, not present in the run data store210, based on a prediction that the suggested values for the one or moresecond parameterized inputs will alter a numerical attribute of theproduct of such process runs (640). In some embodiments, the numericalattribute is a reduction in variance in the one or more firstparameterized inputs (642). Such an embodiment is utilized, for example,to identify situations in which the input space covered by theparameterized resource inputs 310 of the nodes in the process runs isinsufficient to find a correlation between certain process variablesacross the process runs previously executed with a sufficiently highdegree of confidence, or any correlation at all. In these instances,suggested values for the input space that is covered by theparameterized resource inputs 310 are provided in order to test for acorrelation. Such an embodiment is utilized, in other examples, when apotential problem is identified from analysis of existing process runs.In such embodiments, proposed additions to the input space not presentin the process runs in the run data store are made that will facilitatedetermining whether the potential problem is real. If the potentialproblem is real, a new version of the process can be developed thatfurther defines a state (property) of an input or output to an existingor new node in the process in order to attempt to remove process stateambiguity and thereby stabilize the process.

In some embodiments the query identifies one or more third parameterizedinputs and/or parameterized outputs present in runs in the run datastore, and the above-described numerical attribute is a confidence in acorrelation between the first parameterized inputs and/or outputs andthe third parameterized inputs and/or outputs (644). In someembodiments, the one or more processes is a plurality of processes andthe query further identifies a single process in the plurality ofprocesses whose process runs are to be formatted by the statisticsmodule (646). In such embodiments, all the process runs identified bythe query make the same product or produce the same form of analyticalinformation.

In some embodiments, the query further identifies a subset of processruns in the one or more processes (648). In such embodiments, there isno requirement that all the process runs identified by the query makethe same product or produce the same form of analytical information. Infact, some of the process runs responsive to the query may makedifferent products or produce different types of analytical information.

In some embodiments, the statistics module further identifies acorrelation between (i) a first set comprising one or more process runsin the run data store and (ii) a second set comprising one or moreprocess runs in the run data store, where process runs in the second setare not in the first set (650). For instance, in some embodiments, thecorrelation is computed across a plurality of parameterized inputsand/or parameterized outputs present in the first and second sets (652).

Referring to FIG. 6E, optionally, the one or more first parameterizedresource inputs and/or parameterized resource outputs are exported foranalysis to another device (654), e.g., as one or more tab delimitedfiles, CSV files, EXCEL spreadsheets, GOOGLE Sheets, or in a formsuitable for an SQL database.

Optionally, in some embodiments, as discussed above in relation to FIG.5, in some embodiments a process evaluation module is maintained thatgenerates an alert in the form of a computer data transmission when anobtained value for an output property of a parameterized resource outputin a set of parameterized resource outputs for a node in a hypergraph ofa process version is outside the output specification limit (656).

Optionally, in some embodiments a data driver 218 is executed for arespective process in the one or more processes (658). The data driverincludes instructions for receiving a dataset for the respective processand further includes instructions for parsing the dataset to therebyobtain (i) an identification of a process run in the run data store and(ii) output property values associated with the respective set ofparameterized resource outputs of a first node in the hypergraph of therespective process for the process run. The data driver further includesinstructions for populating the output property values of parameterizedresource outputs of the first node in the run data store with the parsedvalues. For instance, in some embodiments, a sync engine associated witha node in the process monitors an associated synced folder. In someembodiments, the sync engine associated with the node runs as abackground process (like Google Drive or Dropbox Sync) on any PCattached to an instrument associated with the node. When new instrumentdata files are added to the folder, the software parses and sends thedata to the data driver 218. In some embodiments, association of thedata sets to the correct protocol variables (parameterized resourceoutputs) of process runs is done via interaction with a user who ispresented with a notification containing choices of process runs towhich they have access. In some embodiments, the data driver 218 alreadycontains the associations between values in the data sets and thecorrect protocol variables (parameterized resource inputs and/oroutputs) of process runs.

In some embodiments, data in the set of parameterized resource outputs314 that is communicated to the computer system for a node 504 of aprocess run 502 comprises a node identifier 406 (e.g., an instrumentidentifier such as a Bluetooth UUID), an identification of a processversion 404, and a value for a parameterized resource input 410. In someembodiments the data is in the form of a JSON structure. Seehttp://json.org/.

Another aspect of the present disclosure provides a computer system 200comprising one or more processors 274, memory 192/290, one or moreprograms stored in the memory for execution by the one or moreprocessors. The one or more programs comprise instructions formaintaining a hypergraph data store 204. The hypergraph data store 204comprises, for each respective process 206 in the one or more processes,a respective plurality of versions 208 of the respective process. Eachrespective version 208 comprises a hypergraph 302 comprising a pluralityof nodes 304 connected by edges 322 in a plurality of edges. Eachrespective node 304 in the plurality of nodes comprises a process stagelabel 306 representing a respective stage in the corresponding process206. Each respective node 304 in the plurality of nodes is associatedwith a set of parameterized resource inputs 308 to the respective stage306 in the corresponding process 206. At least one parameterizedresource input 310 in the set of parameterized resource inputs 308 isassociated with one or more input properties 312. The one or more inputproperties include an input specification limit 314. Each respectivenode 304 in the plurality of nodes is also associated with a set ofparameterized resource outputs 314 to the respective stage 306 in thecorresponding process 206. At least one parameterized resource output316 in the set of parameterized resource outputs 314 is associated withone or more output properties 318. The one or more output properties 318include a corresponding output specification limit 320. Each edge 322 inthe plurality of edges specifies that the set of parameterized resourceoutputs 314 of a node 304 in the plurality of nodes is included in theset of parameterized resource inputs 308 of at least one other node 304in the plurality of nodes. The one or more programs further compriseinstructions for maintaining a run data store 210. The run data store210 comprises a plurality of process runs 402. Each process run 402comprises (i) an identification of a process version 404 in theplurality of versions for a process 206 in the one or more processes,(ii) values for the respective set of parameterized inputs 408 (FIG. 4)of a first node 304 in the hypergraph 302 of the respective version 208and their associated input properties 410, (iii) the respective set ofparameterized resource outputs 412 of the first node 304, and (iv)obtained values of at least one output property 416 of a parameterizedresource output 414 in the respective set of parameterized resourceoutputs of the first node. The one or more programs further compriseinstructions for maintaining a statistics module 212 that, responsive toreceiving a query that identifies one or more first parameterized inputsand/or parameterized outputs present in one or more process runs 402 inthe run data store, formats the one or more first parameterized inputsand/or parameterized outputs for statistical analysis. In this wayinstances (process runs) of a process can be performed with satisfactoryreproducibility.

Embodiments in which Nodes are Connected by Generic Connectors (Edges)with Resource Lists Associated with Those Edges.

Details regarding a flow chart of processes and features of a network,in accordance with another embodiment of the present disclosure, aredisclosed with reference to FIG. 27.

As illustrated in block 2702 of FIG. 27A, a hypergraph data store 204 ismaintained. The hypergraph data store 204 comprises, for each respectiveprocess 206 of one or more processes, a respective plurality of versionsof the respective process. Each respective version 208 comprises ahypergraph 302 comprising a plurality of nodes 304 connected by edges322 in a plurality of edges. Each respective node 304 in the pluralityof nodes comprises a process stage label representing a respective stagein the corresponding process.

FIG. 7 illustrates a process version 208. The process version includes ahypergraph that includes a plurality of nodes 304 corresponding torespective stages of a process (e.g., “Fermenter Prep,” “FermenterSetup,” “Media Prep,” “Grow Inoculum,” “Innoculate Fermenter,”“Fed-Batch Fermentation,” and “Measure T, Ph, D, DO”). In someembodiments, concurrency is supported. That is, multiple users, eachoperating at a different client computer in communication with computersystem 200, can view an instance of the process version displayed inFIG. 7, make changes to it, and view and analyze data from process runsthat make use of it.

In the embodiment in accordance with FIG. 27, each respective edge 322in the plurality of edges is associated with a set of parameterizedresources. Each respective parameterized resource in the correspondingset of parameterized resources is associated with at least acorresponding output of the at least one output of a first node in theplurality of nodes and also is associated with at least a correspondinginput of the one or more inputs of at least one other node in theplurality of nodes. For instance, in embodiments in accordance with FIG.27, a set of parameterized resources (not shown) is associated with edge322-2 of FIG. 7. As such, the set of parameterized resources associatedwith edge 322-2 is associate with a first output of node 304-2 and alsois associated a first input of node 304-3. Thus, when a resource isplaced on an edge (instead of on a node), it need not encompass theoutputs and inputs of the nodes connected to the edge. Theoutputs/inputs (absent any resource specification) can still be on therespective nodes that are connected by the edge, and then the resourcecan be placed on the edge which thus associates it with both the outputand input. The resource can than specify detailed attributes of theoutputs/inputs with which it is associated via the edge. As such, thevalues of a resource associated with the output side of an edge may bedifferent than the values of a resource associated with the input sideof the same edge. In some embodiments, a user can simply click on a node304 to see their inputs and outputs. Moreover, unstructured data in theform of videos, pictures, or comments can be added to nodes 304. Forexample, a video showing the proper way to perform a procedureassociated with a node can be linked to a node by simply dragging anicon link to the video onto the representation of node 304. For example,a video on the proper way perform a fermenter setup can be dragged ontothe “Fermenter Setup” node 304-4 of FIG. 7. Thereafter, when a userclicks on node 304-4, the video is played.

As discussed above, versions 208 of a process 206 are related to eachother. In some embodiments, each version 208 of a process 604 producesthe same product. However, typically a first version and a secondversion in a respective plurality of versions for a process differ fromeach other in some way, such as in a number of nodes, a process stagelabel of a node, a parameterized resource in a set of parameterizedresources, to name some possibilities (2704).

In some embodiments a resource 310 is a single resource. In someembodiments, a resource is a composite resource. Examples of compositeresources include, but are not limited, to mixtures of compositions(e.g., media, broth, etc.) and multi-component equipment (2710).

Referring to FIG. 27B, in some embodiments, the set of parameterizedresources for a first edge in the plurality of edges of a hypergraph 302of a process version 208 in the respective plurality of process versionscomprises a first parameterized resource and this first parameterizedresource specifies a process condition associated with the correspondingstage of the process associated with the edge (2712). For example, insome embodiments, this process condition is a temperature, an exposuretime, a mixing time, a concentration, a type of equipment or a batchidentifier (2714).

As noted above, for a given edge, at least one of the resources in theset of parameterized resources for the edge is associated with one ormore properties, and the one or more properties includes a correspondingspecification limit. In some embodiments, this correspondingspecification limit comprises an upper limit and a lower limit for thecorresponding parameterized resource (2716). To illustrate, an exampleof a property is pH of a composition. In such an example, thespecification limit specifies the allowed upper limit for the pH of thecomposition and the allowed lower limit for the pH of the composition.In alternative embodiments, this corresponding specification limitcomprises an enumerated list of allowable types (2718). To illustrate,an example of a property is a crystallographic orientation of amaterial. In such an example, the specification limit specifies anenumerated list of allowed crystallographic orientations for thematerial.

In some embodiments, the one or more processes in a hypergraph datastore is, in fact, a plurality of processes. Further, a first process inthe plurality of processes results in a first product and a secondprocess in the plurality of processes results in a different secondproduct (2720). For instance, a first process in the hypergraph datastore may result in the manufacture of one type of composition andanother process in the hypergraph data store may result in themanufacture of another composition.

Referring to block 2722 of FIG. 27B, a run data store 210 is alsomaintained. The run data store comprises a plurality of process runs402. In typical embodiments, a process version 208 is locked before aprocess run 402 for the process version 208 is executed so that nofurther changes can be made to the process version 208. If changes tounderlying process 206 are desired, a new process version 208 is definedin such embodiments.

Each process run 402 comprises an identification of a first node of aprocess version 404 (208) in the plurality of versions for a process 206in the one or more processes, as illustrated in FIG. 4. Furthermore,each process run 402 comprises values for the respective set ofparameterized resources and their associated one or more propertiescorresponding to at least one of the first output edge in the pluralityof edges of the hypergraph 302 of the respective version. In someembodiments values for the properties of the parameterized resources inthe set of parameterized resources of an edge in the hypergraph 302 ofthe respective version are provided in a process run.

In some embodiments, the run data store 210 further comprises agenealogical graph 420 showing a relationship between (i) versions of asingle process in the plurality of versions of a process that are in theplurality of process runs or (ii) versions of two or more processes inthe respective plurality of versions of two or more processes that arein the plurality of process runs (2724). For instance, in someembodiments, a first process version 404 in a process set 420 and asecond process version 404 in the process set 420 have the samehypergraph but a property or specification limit to one of the edges inthe hypergraph is different. In another example, a first process version404 in a process set 420 and a second process version 404 in the processset 420 have hypergraphs that have all but one, all but two, all butthree, all but four nodes, and so forth, in common. The genealogicalgraph provides an advantageous way of discerning the relationshipbetween the various process versions of a given process.

Turning to FIG. 27C, a statistics module 212 is also maintained (2726).The statistics module 212 leverages the structure of run data store 210and hypergraph data store 204 to enable analytics of process runs. Inparticular, the statistics module 212 combined with the unique structureof run data store 210 and hypergraph data store 204 provides anadvantageous platform for supporting statistical process control (SPC)over the many disparate components of a process 206 and thus providespowerful tools for analyzing and stabilizing such processes. SPC is amethod of quality control which uses statistical methods. It is appliedin order to monitor and control processes. Monitoring and controllingprocesses ensures that they operate at their full potential. Forinstance, at its full potential, a process 206 can make as muchconforming product as possible with a minimization of waste. SPC can beapplied to any process 206 where the “conforming product” (productmeeting specifications) output can be measured. SPC makes use of controlcharts, a focus on continuous improvement and the design of process runs402 (e.g., experiments). See, for example, Barlow and Irony, 1992,“Foundations of statistical quality control” in Ghosh, M. & Pathak, P.K. (eds.) Current Issues in Statistical Inference: Essays in Honor of D.Basu, Hayward, Calif., Institute of Mathematical Statistics, pp. 99-112,which is hereby incorporated by reference.

Advantageously, rather than having to track down the disparate data indisparate forms associated with a process or, rather the process runsthat make use of the nodes of the process, in order to support SPC, thestatistics module 212, responsive to receiving a query that identifiesone or more first parameterized resources present in one or more processruns in the run data store, is able to easily retrieve and format theone or more resources for analysis. In some embodiments, for example,the data is formatted as one or more tab delimited files, CSV files,EXCEL spreadsheets, GOOGLE Sheets, and/or in a form suitable forrelational databases. In particular, the data is structured to ensurethat such data can be efficiently analyzed so that potentialcorrelations are not overlooked in subsequent analysis. An example ofsuch analysis that is performed as part of SPC is correlation analysissuch as the root cause analysis illustrated in FIG. 26. Root causeanalysis is described, for example, in Wilson et al., 1993, Root CauseAnalysis: A Tool for Total Quality Management, Milwaukee, Wis., ASQQuality Press. pp. 8-17, which is hereby incorporated by reference.Leading up to the root cause analysis illustrated in FIG. 26, a queryidentifies one or more first parameterized resources (e.g., amino acidtype) present in one or more process runs in the run data store. Datafor the one or more first parameterized resources is then formatted andoutputted for analysis. With this formatted data, an analysis, such asthe root cause analysis of FIG. 26, is conducted. In some embodiments,the query results are formatted for a third party statistical analysispackage such as JMP (SAS, Buckinghamshire, England,http://www.jmp.com/en_dk/software.html) Analysis using such a thirdparty statistical analysis package typically results in proposals fornew process versions, in which nodes are added or removed, or the setsof resources associated with edges to existing nodes are further definedor redefined, in order to identify and remove unwanted processvariability (e.g., to stabilize the process).

The query can be of any of the resources available for any combinationof process versions of any combination of the one or more processes inthe run data store 210 or properties of these resources. As such, insome embodiments, the query further identifies one or more secondparameterized resources present in one or more runs in the run datastore (or properties thereof) and the one or more first resources andthe one or more second resources are correlated and a numerical measureof this correlation is formatted for presentation (2728). In someembodiments, the numerical measure of correlation is on a scale betweena low number and a high number, where the low number (e.g., zero) isindicative of no correlation and the high number (e.g., one) isindicative of complete correlation across the one or more firstparameterized resources and the one or more second parameterizedresources.

In some embodiments, the query further identifies one or more secondresources present (or their properties) in one or more runs in the rundata store, and the statistics module further identifies a correlationbetween (i) the one or more first parameterized resources and (ii) theone or more second parameterized resources present in one or moreprocess runs in the run data store from among all the parameterizedresources present in the run data store using a multivariate analysistechnique (2730).

In some embodiments, the query identifies (i) one or more properties ofone or more first resources and (ii) one or more properties of one ormore second resources present in one or more runs in the run data store,and the statistics module further seeks a correlation between (i) theidentified properties of the one or more first resources and (ii) theidentified one or more properties of the one or more second resourcespresent in one or more process runs in the run data store from among allthe parameterized resources present in the run data store using amultivariate analysis technique.

In some embodiments, the above processes invoke a multivariate analysistechnique that comprises a feature selection technique (2732) (e.g.,least angle regression, stepwise regression). Feature selectiontechniques are particularly advantageous in identifying, from among themultitude of variables (e.g., values for properties of resources in setsof resources associated with edges) present across sets of process runs,which variables (e.g., which properties of resources of which edges)have a significant causal effect on a property of the product of theprocess (e.g., which of the variables are causal for poorreproducibility, poor yield, or conversely which of the variables arecausal for excellent reproducibility, higher yield). Feature selectiontechniques are described, for example, in Saeys et al., 2007, “A reviewof feature selection techniques in bioinformatics,” Bioinformatics 23,2507-2517, and Tibshirani, 1996, “Regression and Shrinkage and Selectionvia the Lasso,” J. R. Statist. Soc B, pp. 267-288, each of which ishereby incorporated by reference.

In some embodiments, the one or more processes are a plurality ofprocesses and the correlation is identified from process runs in asubset of the plurality of processes (2734). There is no requirementthat each of the processes across which this correlation is identifiedmake the same product in such embodiments. Such embodiments are highlyadvantageous because they allow for the investigation of undesirableprocess variability across process runs used in the manufacture ofdifferent products. For instance, some of the process runs used in acorrelation analysis may manufacture biologic A and other process runsused in the same correlation analysis may manufacture biologic B.Correlation analysis that uses data from process runs for biologics Aand B allows for the investigation of causes of variation that areproduct independent, such as, for example, a poorly defined fermentationstep. For example, the sugar input into this fermentation step in theprocess runs for both biologics A and B may not be adequately defined toensure process stabilization. Another example of a source of variationcommon to these process versions could be, for example, identifiedthrough correlation analysis across process runs for both biologics Aand B, to a piece of equipment that is beginning to fail due to age.This is all possible because the disclosed systems and methodsadvantageously impose a consistent framework to the process runs thatmake different products. Thus, it is possible to aggregate process runsfrom across different products and perform cross-sectional filtering onany desirable set of resources and properties of resources, orspecification limits thereof in these process runs, in order to, forexample, discover sources of process variability that are independent(or dependent) of actual products made by such processes.

In some embodiments, the one or more processes are a plurality ofprocesses and the correlation is identified from process runs in asingle process in the plurality of processes (2736). In suchembodiments, each of the processes across which this correlation isidentified makes the same product or produce the same analyticalinformation. Such embodiments are used, for example, to preciselyidentify key sources of variability in the manufacture of the product orproduction of the analytical information through the process.

In some embodiments, the one or more processes is a plurality ofprocesses and the query further identifies a subset of the plurality ofprocesses whose process runs are to be formatted by the statisticsmodule (2738).

Turning to FIG. 27D, in some embodiments the statistics module 212further provides suggested values (e.g., limits) for the one or moresecond parameterized resources for one or more additional process runsof a first process in the one or more processes, not present in the rundata store 210, based on a prediction that the suggested values (e.g.,limits) for the one or more second parameterized resources will alter anumerical attribute of the product of such process runs (2740). In someembodiments, the numerical attribute is a reduction in variance in theone or more first parameterized resources (2742). Such an embodiment isutilized, for example, to identify situations in which the resourcespace covered by the parameterized resources of the edges in the processruns is insufficient to find a correlation between certain processvariables across the process runs previously executed with asufficiently high degree of confidence, or any correlation at all. Inthese instances, suggested values for the space that is covered by theparameterized resources are provided in order to test for a correlation.Such an embodiment is utilized, in other examples, when a potentialproblem is identified from analysis of existing process runs. In suchembodiments, proposed additions to the resource space not present in theprocess runs in the run data store are made that will facilitatedetermining whether the potential problem is real. If the potentialproblem is real, a new version of the process can be developed thatfurther defines a state (property) of a resource of an existing or newedge in the process in order to attempt to remove process stateambiguity and thereby stabilize the process.

In some embodiments the query identifies one or more third parameterizedresources present in runs in the run data store, and the above-describednumerical attribute is a confidence in a correlation between the firstresources and the third parameterized resources (2744). In someembodiments, the one or more processes is a plurality of processes andthe query further identifies a single process in the plurality ofprocesses whose process runs are to be formatted by the statisticsmodule (2746). In such embodiments, all the process runs identified bythe query make the same product or produce the same form of analyticalinformation.

In some embodiments, the query further identifies a subset of processruns in the one or more processes (2748). In such embodiments, there isno requirement that all the process runs identified by the query makethe same product or produce the same form of analytical information. Infact, some of the process runs responsive to the query may makedifferent products or produce different types of analytical information.

In some embodiments, the statistics module further identifies acorrelation between (i) a first set comprising one or more process runsin the run data store and (ii) a second set comprising one or moreprocess runs in the run data store, where process runs in the second setare not in the first set (2750). For instance, in some embodiments, thecorrelation is computed across a plurality of parameterized resourcespresent in the first and second sets (2752).

Referring to FIG. 27E, optionally, the one or more first parameterizedresources are exported for analysis to another device (2754), e.g., asone or more tab delimited files, CSV files, EXCEL spreadsheets, GOOGLESheets, or in a form suitable for an SQL database.

Optionally, in some embodiments, as discussed above in relation to FIG.5, in some embodiments a process evaluation module is maintained thatgenerates an alert in the form of a computer data transmission when anobtained value for a property of a parameterized resource in a set ofparameterized resources for an edge in a hypergraph of a process versionis outside the specification limit of the resource (2756).

Optionally, in some embodiments a data driver 218 is executed for arespective process in the one or more processes (2758). The data driverincludes instructions for receiving a dataset for the respective processand further includes instructions for parsing the dataset to therebyobtain (i) an identification of a process run in the run data store and(ii) property values associated with the respective set of parameterizedresources of a first edge in the hypergraph of the respective processfor the process run. The data driver further includes instructions forpopulating the property values of parameterized resources of the firstedge in the run data store with the parsed values. For instance, in someembodiments, a sync engine associated with an edge in the processmonitors an associated synced folder. In some embodiments, the syncengine associated with the edge runs as a background process (likeGoogle Drive or Dropbox Sync) on any PC attached to an instrumentassociated with the edge. When new instrument data files are added tothe folder, the software parses and sends the data to the data driver218. In some embodiments, association of the data sets to the correctprotocol variables (parameterized resources) of process runs is done viainteraction with a user who is presented with a notification containingchoices of process runs to which they have access. In some embodiments,the data driver 218 already contains the associations between values inthe data sets and the correct protocol variables (parameterizedresources) of process runs.

Embodiments in which Nodes are Connected by Generic Connectors (Edges)with No Associated Lists.

Details regarding a flow chart of processes and features of a network,in accordance with another embodiment of the present disclosure, aredisclosed with reference to FIG. 28.

As illustrated in block 2802 of FIG. 28A, a hypergraph data store 204 ismaintained. The hypergraph data store 204 comprises, for each respectiveprocess 206 of one or more processes, a respective plurality of versionsof the respective process. Each respective version 208 comprises ahypergraph 302 comprising a plurality of nodes 304 connected by edges322 in a plurality of edges. Each respective node 304 in the pluralityof nodes comprises a process stage label representing a respective stagein the corresponding process.

FIG. 7 illustrates a process version 208. The process version includes ahypergraph that includes a plurality of nodes 304 corresponding torespective stages of a process (e.g., “Fermenter Prep,” “FermenterSetup,” “Media Prep,” “Grow Inoculum,” “Innoculate Fermenter,”“Fed-Batch Fermentation,” and “Measure T, Ph, D, DO”). In someembodiments, concurrency is supported. That is, multiple users, eachoperating at a different client computer in communication with computersystem 200, can view an instance of the process version displayed inFIG. 7, make changes to it, and view and analyze data from process runsthat make use of it.

In the embodiment in accordance with FIG. 28, each respective edge 322in the plurality of edges comprises at least one output of a first nodein the plurality of nodes and also comprises a first input of the one ormore inputs of at least one other node in the plurality of nodes. Insome embodiments, a user can simply click on a node 304 to see theirinputs and outputs. Moreover, unstructured data in the form of videos,pictures, or comments can be added to nodes 304. For example, a videoshowing the proper way to perform a procedure associated with a node canbe linked to a node by simply dragging an icon link to the video ontothe representation of node 304. For example, a video on the proper wayperform a fermenter setup can be dragged onto the “Fermenter Setup” node304-4 of FIG. 7. Thereafter, when a user clicks on node 304-4, the videois played.

Referring to block 2804 of FIG. 28A, a run data store 210 is alsomaintained. The run data store comprises a plurality of process runs402. In typical embodiments, a process version 208 is locked before aprocess run 402 for the process version 208 is executed so that nofurther changes can be made to the process version 208. If changes tounderlying process 206 are desired, a new process version 208 is definedin such embodiments. Referring to block 2806 of FIG. 28A, in someembodiments, each respective edge in the plurality of edges isassociated with a corresponding set of parameterized resources. Thecorresponding set of parameterized resources comprises a first output ofthe at least one output of a first node in the plurality of nodes andalso comprises a first input of the one or more inputs of at least oneother node in the plurality of nodes. At least one parameterizedresource in the set of parameterized resources is associated with one ormore properties. The one or more properties include one or morecorresponding specification limits. The run data store furthercomprises, for each respective process run in the plurality of processruns, values for the respective set of parameterized resourcescorresponding to at least one of the first output of the first node orthe first input of the at least one other node of a first edge in thehypergraph of the respective version and their associated one or moreproperties.

As discussed above, versions 208 of a process 206 are related to eachother. In some embodiments, each version 208 of a process 604 producesthe same product. However, typically a first version and a secondversion in a respective plurality of versions for a process differ fromeach other in some way, such as in a number of nodes, a process stagelabel of a node, a parameterized resource in a set of parameterizedresources, to name some possibilities (2808).

Referring to block 2810 of FIG. 28A, in some embodiments the set ofparameterized resources for an edge in the plurality of edges of ahypergraph for a process version in the respective plurality of processversions comprises a first and second parameterized resource. The firstparameterized resource specifies a first resource and is associated witha first property, and the second parameterized resource specifies asecond resource and is associated with a second property. Referring toblock 2812, in some such embodiments, the first property is a viscosityvalue, a purity value, composition value, a temperature value, a weightvalue, a mass value, a volume value, or a batch identifier of the firstresource. In some embodiments a resource 310 is a single resource. Insome embodiments, a resource is a composite resource. Examples ofcomposite resources include, but are not limited, to mixtures ofcompositions (e.g., media, broth, etc.) and multi-component equipment(2814).

Referring to FIG. 28B, in some embodiments, the set of parameterizedresources for a first edge in the plurality of edges of a hypergraph 302of a process version 208 in the respective plurality of process versionscomprises a first parameterized resource and this first parameterizedresource specifies a process condition (2816). For example, in someembodiments, this process condition is a temperature, an exposure time,a mixing time, a concentration, a type of equipment or a batchidentifier (2818).

As noted above at least one of resource in a set of parameterizedresources is associated with one or more properties, and the one or moreproperties includes a corresponding specification limit. In someembodiments, this corresponding specification limit comprises an upperlimit and a lower limit for the corresponding parameterized resource(2820). To illustrate, an example of a property is pH of a composition.In such an example, the specification limit specifies the allowed upperlimit for the pH of the composition and the allowed lower limit for thepH of the composition. In alternative embodiments, this correspondingspecification limit comprises an enumerated list of allowable types(2822). To illustrate, an example of a property is a crystallographicorientation of a material. In such an example, the specification limitspecifies an enumerated list of allowed crystallographic orientationsfor the material.

In some embodiments, the one or more processes in a hypergraph datastore is, in fact, a plurality of processes. Further, a first process inthe plurality of processes results in a first product and a secondprocess in the plurality of processes results in a different secondproduct (2824). For instance, a first process in the hypergraph datastore may result in the manufacture of one type of composition andanother process in the hypergraph data store may result in themanufacture of another composition.

In some embodiments, the run data store 210 further comprises agenealogical graph 420 showing a relationship between (i) versions of asingle process in the plurality of versions of a process that are in theplurality of process runs or (ii) versions of two or more processes inthe respective plurality of versions of two or more processes that arein the plurality of process runs (2826). For instance, in someembodiments, a first process version 404 in a process set 420 and asecond process version 404 in the process set 420 have the samehypergraph but a property or specification limit to one of the edges inthe hypergraph is different. In another example, a first process version404 in a process set 420 and a second process version 404 in the processset 420 have hypergraphs that have all but one, all but two, all butthree, all but four nodes, and so forth, in common. The genealogicalgraph provides an advantageous way of discerning the relationshipbetween the various process versions of a given process.

Turning to FIG. 28C, a statistics module 212 is also maintained (2828).The statistics module 212 leverages the structure of run data store 210and hypergraph data store 204 to enable analytics of process runs. Inparticular, the statistics module 212 combined with the unique structureof run data store 210 and hypergraph data store 204 provides anadvantageous platform for supporting statistical process control (SPC)over the many disparate components of a process 206 and thus providespowerful tools for analyzing and stabilizing such processes. SPC is amethod of quality control which uses statistical methods. It is appliedin order to monitor and control processes. Monitoring and controllingprocesses ensures that they operate at their full potential. Forinstance, at its full potential, a process 206 can make as muchconforming product as possible with a minimization of waste. SPC can beapplied to any process 206 where the “conforming product” (productmeeting specifications) output can be measured. SPC makes use of controlcharts, a focus on continuous improvement and the design of process runs402 (e.g., experiments). See, for example, Barlow and Irony, 1992,“Foundations of statistical quality control” in Ghosh, M. & Pathak, P.K. (eds.) Current Issues in Statistical Inference: Essays in Honor of D.Basu, Hayward, Calif., Institute of Mathematical Statistics, pp. 99-112,which is hereby incorporated by reference.

Advantageously, rather than having to track down the disparate data indisparate forms associated with a process or, rather the process runsthat make use of the nodes of the process, in order to support SPC, thestatistics module 212, responsive to receiving a query that identifiesone or more first parameterized resources present in one or more processruns in the run data store, is able to easily retrieve and format theone or more resources for analysis. In some embodiments, for example,the data is formatted as one or more tab delimited files, CSV files,EXCEL spreadsheets, GOOGLE Sheets, and/or in a form suitable forrelational databases. In particular, the data is structured to ensurethat such data can be efficiently analyzed so that potentialcorrelations are not overlooked in subsequent analysis. An example ofsuch analysis that is performed as part of SPC is correlation analysissuch as the root cause analysis illustrated in FIG. 26. Root causeanalysis is described, for example, in Wilson et al., 1993, Root CauseAnalysis: A Tool for Total Quality Management, Milwaukee, Wis., ASQQuality Press. pp. 8-17, which is hereby incorporated by reference.Leading up to the root cause analysis illustrated in FIG. 26, a queryidentifies one or more first parameterized resources (e.g., amino acidtype) present in one or more process runs in the run data store. Datafor the one or more first parameterized resources is then formatted andoutputted for analysis. With this formatted data, an analysis, such asthe root cause analysis of FIG. 26, is conducted. In some embodiments,the query results are formatted for a third party statistical analysispackage such as JMP (SAS, Buckinghamshire, England, on the Internet atjmp.com/en_dk/software.html). Analysis using such a third partystatistical analysis package typically results in proposals for newprocess versions, in which nodes are added or removed, or the sets ofresources associated with edges to existing nodes are further defined orredefined, in order to identify and remove unwanted process variability(e.g., to stabilize the process).

The query can be of any of the resources available for any combinationof process versions of any combination of the one or more processes inthe run data store 210 or properties of these resources. As such, insome embodiments, the query further identifies one or more secondparameterized resources present in one or more runs in the run datastore (or properties thereof) and the one or more first resources andthe one or more second resources are correlated and a numerical measureof this correlation is formatted for presentation (2830). In someembodiments, the numerical measure of correlation is on a scale betweena low number and a high number, where the low number (e.g., zero) isindicative of no correlation and the high number (e.g., one) isindicative of complete correlation across the one or more firstparameterized resources and the one or more second parameterizedresources.

In some embodiments, the query further identifies one or more secondresources present (or their properties) in one or more runs in the rundata store, and the statistics module further identifies a correlationbetween (i) the one or more first parameterized resources and (ii) theone or more second parameterized resources present in one or moreprocess runs in the run data store from among all the parameterizedresources present in the run data store using a multivariate analysistechnique (2830).

In some embodiments, the query identifies a correlation between (i) oneor more first parameterized resources and (ii) one or more secondparameterized resources present in one or more process runs in the rundata store from among all the parameterized resources present in the rundata store using a multivariate analysis technique (2832). In someembodiments, the above processes invoke a multivariate analysistechnique that comprises a feature selection technique (2834) (e.g.,least angle regression, stepwise regression). Feature selectiontechniques are particularly advantageous in identifying, from among themultitude of variables (e.g., values for properties of resources in setsof resources associated with edges) present across sets of process runs,which variables (e.g., which properties of resources) have a significantcausal effect on a property of the product of the process (e.g., whichof the variables are causal for poor reproducibility, poor yield, orconversely which of the variables are causal for excellentreproducibility, higher yield). Feature selection techniques aredescribed, for example, in Saeys et al., 2007, “A review of featureselection techniques in bioinformatics,” Bioinformatics 23, 2507-2517,and Tibshirani, 1996, “Regression and Shrinkage and Selection via theLasso,” J. R. Statist. Soc B, pp. 267-288, each of which is herebyincorporated by reference.

In some embodiments, the one or more processes are a plurality ofprocesses and the correlation is identified from process runs in asubset of the plurality of processes (2836). There is no requirementthat each of the processes across which this correlation is identifiedmake the same product in such embodiments. Such embodiments are highlyadvantageous because they allow for the investigation of undesirableprocess variability across process runs used in the manufacture ofdifferent products. For instance, some of the process runs used in acorrelation analysis may manufacture biologic A and other process runsused in the same correlation analysis may manufacture biologic B.Correlation analysis that uses data from process runs for biologics Aand B allows for the investigation of causes of variation that areproduct independent, such as, for example, a poorly defined fermentationstep. For example, the sugar input into this fermentation step in theprocess runs for both biologics A and B may not be adequately defined toensure process stabilization. Another example of a source of variationcommon to these process versions could be, for example, identifiedthrough correlation analysis across process runs for both biologics Aand B, to a piece of equipment that is beginning to fail due to age.This is all possible because the disclosed systems and methodsadvantageously impose a consistent framework to the process runs thatmake different products. Thus, it is possible to aggregate process runsfrom across different products and perform cross-sectional filtering onany desirable set of resources and properties of resources, orspecification limits thereof in these process runs, in order to, forexample, discover sources of process variability that are independent(or dependent) of actual products made by such processes.

In some embodiments, the one or more processes are a plurality ofprocesses and the correlation is identified from process runs in asingle process in the plurality of processes (2838). In suchembodiments, each of the processes across which this correlation isidentified makes the same product or produce the same analyticalinformation. Such embodiments are used, for example, to preciselyidentify key sources of variability in the manufacture of the product orproduction of the analytical information through the process.

In some embodiments, the one or more processes is a plurality ofprocesses and the query further identifies a subset of the plurality ofprocesses whose process runs are to be formatted by the statisticsmodule (2839).

Turning to FIG. 28D, in some embodiments the statistics module 212further provides suggested values (e.g., limits) for the one or moresecond parameterized resources for one or more additional process runsof a first process in the one or more processes, not present in the rundata store 210, based on a prediction that the suggested values (e.g.,limits) for the one or more second parameterized resources will alter anumerical attribute of the product of such process runs (2840). In someembodiments, the numerical attribute is a reduction in variance in theone or more first parameterized resources exhibited across the pluralityof runs (2742). Such an embodiment is utilized, for example, to identifysituations in which the resource space covered by the parameterizedresources in the process runs is insufficient to find a correlationbetween certain process variables across the process runs previouslyexecuted with a sufficiently high degree of confidence, or anycorrelation at all. In these instances, suggested values for the spacethat is covered by the parameterized resources are provided in order totest for a correlation. Such an embodiment is utilized, in otherexamples, when a potential problem is identified from analysis ofexisting process runs. In such embodiments, proposed additions to theresource space not present in the process runs in the run data store aremade that will facilitate determining whether the potential problem isreal. If the potential problem is real, a new version of the process canbe developed that further defines a state (property) of a resource of anexisting or new edge in the process in order to attempt to removeprocess state ambiguity and thereby stabilize the process.

In some embodiments the query identifies one or more third parameterizedresources present in runs in the run data store, and the above-describednumerical attribute is a confidence in a correlation between the firstresources and the third parameterized resources (2844). In someembodiments, the one or more processes is a plurality of processes andthe query further identifies a single process in the plurality ofprocesses whose process runs are to be formatted by the statisticsmodule (2846). In such embodiments, all the process runs identified bythe query make the same product or produce the same form of analyticalinformation.

In some embodiments, the query further identifies a subset of processruns in the one or more processes (2848). In such embodiments, there isno requirement that all the process runs identified by the query makethe same product or produce the same form of analytical information. Infact, some of the process runs responsive to the query may makedifferent products or produce different types of analytical information.

In some embodiments, the statistics module further identifies acorrelation between (i) a first set comprising one or more process runsin the run data store and (ii) a second set comprising one or moreprocess runs in the run data store, where process runs in the second setare not in the first set (2850). For instance, in some embodiments, thecorrelation is computed across a plurality of parameterized resourcespresent in the first and second sets (2852).

Referring to FIG. 28E, optionally, the one or more first parameterizedresources are exported for analysis to another device (2754), e.g., asone or more tab delimited files, CSV files, EXCEL spreadsheets, GOOGLESheets, or in a form suitable for an SQL database.

Optionally, in some embodiments, as discussed above in relation to FIG.5, in some embodiments a process evaluation module is maintained thatgenerates an alert in the form of a computer data transmission when anobtained value for a property of a parameterized resource in a set ofparameterized resources for an edge in a hypergraph of a process versionis outside the specification limit of the resource (2856).

Optionally, in some embodiments a data driver 218 is executed for arespective process in the one or more processes (2858). The data driverincludes instructions for receiving a dataset for the respective processand further includes instructions for parsing the dataset to therebyobtain (i) an identification of a process run in the run data store and(ii) property values associated with a respective set of parameterizedresources in the hypergraph of the respective process for the processrun. The data driver further includes instructions for populating theproperty values of parameterized resources of the first edge in the rundata store with the parsed values. For instance, in some embodiments, async engine associated with the process monitors an associated syncedfolder. In some embodiments, the sync engine runs as a backgroundprocess (like Google Drive or Dropbox Sync) on any PC attached to aninstrument associated with the edge. When new instrument data files areadded to the folder, the software parses and sends the data to the datadriver 218. In some embodiments, association of the data sets to thecorrect protocol variables (parameterized resources) of process runs isdone via interaction with a user who is presented with a notificationcontaining choices of process runs to which they have access. In someembodiments, the data driver 218 already contains the associationsbetween values in the data sets and the correct protocol variables(parameterized resources) of process runs.

REFERENCES CITED AND ALTERNATIVE EMBODIMENTS

All references cited herein are incorporated herein by reference intheir entirety and for all purposes to the same extent as if eachindividual publication or patent or patent application was specificallyand individually indicated to be incorporated by reference in itsentirety for all purposes.

The present invention can be implemented as a computer program productthat comprises a computer program mechanism embedded in a nontransitorycomputer readable storage medium. For instance, the computer programproduct could contain the program modules shown in any combination ofFIGS. 1, 2, 3, 4, and/or 5. These program modules can be stored on aCD-ROM, DVD, magnetic disk storage product, or any other tangiblecomputer readable data or program storage product.

Many modifications and variations of this invention can be made withoutdeparting from its spirit and scope, as will be apparent to thoseskilled in the art. The specific embodiments described herein areoffered by way of example only. The embodiments were chosen anddescribed in order to best explain the principles of the invention andits practical applications, to thereby enable others skilled in the artto best utilize the invention and various embodiments with variousmodifications as are suited to the particular use contemplated. Theinvention is to be limited only by the terms of the appended claims,along with the full scope of equivalents to which such claims areentitled.

What is claimed is:
 1. A non-transitory computer readable storage mediumfor analyzing one or more processes, each process in the one or moreprocesses resulting in a respective product or analytical information,wherein the non-transitory computer readable storage medium storesinstructions, which when executed by a first device, cause the firstdevice to: (A) maintain a hypergraph data store comprising, for eachrespective process in the one or more processes, a respective pluralityof versions of the respective process, each respective versioncomprising: a hypergraph comprising a plurality of nodes connected byedges in a plurality of edges, wherein each respective node in theplurality of nodes represents a respective stage in the respectiveprocess, a node in the plurality of nodes is associated with (i) a setof parameterized resource inputs to the respective stage in thecorresponding process, and/or (ii) a set of parameterized resourceoutputs to the respective stage in the corresponding process, and eachrespective edge in the plurality of edges specifies that the set ofparameterized resource outputs of a node in the plurality of nodes isincluded in the set of parameterized resource inputs of at least oneother node in the plurality of nodes; (B) maintain a run data store,wherein the run data store comprises a plurality of process runs, eachprocess run comprising (i) an identification of a version in theplurality of versions for a process in the one or more processes, (ii)values for the respective set of parameterized resource inputs of afirst node in the hypergraph of the respective version, (iii) therespective set of parameterized resource outputs of the first node inthe hypergraph of the respective version, and (iv) obtained values of atleast one output property of a parameterized resource output in therespective set of parameterized resource outputs of the first node inthe hypergraph of the respective version; and (C) execute instructionsfor acquiring and processing data for the one or more processes.
 2. Thenon-transitory computer readable storage medium of claim 1, wherein theinstructions for acquiring and processing data for the one or moreprocesses comprises: executing a data driver for a respective process inthe one or more processes, the data driver including: instructions forreceiving a dataset for the respective process; and instructions forprocessing the dataset.
 3. The non-transitory computer readable storagemedium of claim 2, wherein the instructions for processing the datasetcomprise: instructions for parsing the dataset to thereby obtain (i) anidentification of a process run in the run data store and (ii) inputand/or output property values associated with a respective set ofparameterized resource inputs and/or outputs of a first node in thehypergraph of the respective process for the process run, andinstructions for populating the input and/or output property values ofparameterized resource inputs and/or outputs of the first node in therun data store with the parsed values.
 4. The non-transitory computerreadable storage medium of claim 1, wherein the instructions foracquiring and processing data comprise instructions for generating orchanging one or more parameterized resource inputs, parameterizedresource outputs, process runs, stages, nodes, edges, input properties,output properties, input specification limits of input properties,output specification limits of output properties and/or obtained valuesof input or output properties present in the run data store based on theacquired data.
 5. The non-transitory computer readable storage medium ofclaim 1, wherein the instructions for acquiring and processing data forthe one or more processes comprises instructions for reformatting datatypes present in the run data store.
 6. The non-transitory computerreadable storage medium of claim 1, wherein the instructions foracquiring and processing data for the one or more processes comprisesinstructions for changing a storage medium or a storage format used bythe run data store.
 7. The non-transitory computer readable storagemedium of claim 1, wherein the instructions for acquiring and processingdata for the one or more processes comprises instructions for storingthe acquired data.
 8. The non-transitory computer readable storagemedium of claim 1, wherein the instructions for acquiring and processingdata for the one or more processes comprises instructions for performingan analysis of the one or more processes using the acquired data.
 9. Thenon-transitory computer readable storage medium of claim 8, wherein theinstructions for performing the analysis comprises root cause analysis,correlation analysis, or a feature selection technique.
 10. Thenon-transitory computer readable storage medium of claim 1, wherein theinstructions for acquiring and processing data for the one or moreprocesses comprises instructions for initiating an alert when a specificcondition arises in a process in the one or more processes.
 11. Thenon-transitory computer readable storage medium of claim 1, wherein theinstructions for acquiring and processing data for the one or moreprocesses comprises instructions for initiating instructions on thefirst device responsive to the acquired data.
 12. The non-transitorycomputer readable storage medium of claim 1, wherein the one or moreprocesses comprises a plurality of processes.
 13. The non-transitorycomputer readable storage medium of claim 1, wherein the one or moreversions comprises a plurality of versions.
 14. The non-transitorycomputer readable storage medium of claim 1, wherein the one or moreprocesses comprises a single process.
 15. The non-transitory computerreadable storage medium of claim 1, wherein the one or more versionscomprises a single version.
 16. The non-transitory computer readablestorage medium of claim 1, wherein an input property in the one or moreinput properties associated with a parameterized resource input in theset of parameterized resource inputs to the respective stage in therespective process includes an input specification limit.
 17. Thenon-transitory computer readable storage medium of claim 1, wherein anoutput property in the one or more output properties associated with aparameterized resource output in the set of parameterized resourceoutputs to the respective stage in the respective process includes anoutput specification limit
 18. The non-transitory computer readablestorage medium of claim 1, wherein at least one parameterized resourceinput in the set of parameterized resource inputs is associated with oneor more input properties.
 19. The non-transitory computer readablestorage medium of claim 18, wherein the one or more input propertiesincludes an input specification limit.
 20. The non-transitory computerreadable storage medium of claim 1, wherein the one or more outputproperties consists of a single output property and wherein the singleoutput property is an identifier.
 21. The non-transitory computerreadable storage medium of claim 1, wherein a first version and a secondversion in a respective plurality of versions for a process in the oneor more processes differ from each other in a number of nodes, a processstage label of a node, a parameterized resource input in a set ofparameterized resource inputs, or a parameterized resource output in aset of parameterized resource outputs.
 22. The non-transitory computerreadable storage medium of claim 1, wherein the set of parameterizedresource inputs for a node in the plurality of nodes of a hypergraph fora process version in the respective plurality of process versionscomprises a first and second parameterized resource input, the firstparameterized resource input specifying a first resource and isassociated with a first input property, and the second parameterizedresource input specifying a second resource and is associated with asecond input property, wherein the first input property is differentthan the second input property.
 23. The non-transitory computer readablestorage medium of claim 22, wherein the first input property is aviscosity value, a purity value, composition value, a temperature value,a weight value, a mass value, a volume value, or a batch identifier ofthe first resource.
 24. The non-transitory computer readable storagemedium of claim 22, wherein the first resource is a single resource or acomposite resource.
 25. The non-transitory computer readable storagemedium of claim 1, wherein the set of parameterized resource inputs fora first node in the plurality of nodes of a hypergraph of a processversion in the respective plurality of process versions comprises afirst parameterized resource input, the first parameterized resourceinput specifying a process condition associated with the correspondingstage of the process associated with the first node.
 26. Thenon-transitory computer readable storage medium of claim 25, wherein theprocess condition comprises a temperature, an exposure time, a mixingtime, a type of equipment, or a batch identifier.
 27. The non-transitorycomputer readable storage medium of claim 1, wherein the non-transitorycomputer readable storage medium further stores instructions formaintaining one or more interfaces, wherein each respective interface inthe one or more interfaces acquires data for the run data store from oneor more corresponding instruments.
 28. The non-transitory computerreadable storage medium of claim 27, wherein a respective interface inthe one or more interfaces directs a corresponding instrument to acquiredata for the run data store.
 29. The non-transitory computer readablestorage medium of claim 27, wherein a respective interface in the one ormore interfaces directs a corresponding instrument to acquire values ofinput or output properties.
 30. The non-transitory computer readablestorage medium of claim 27, wherein a respective interface in the one ormore interfaces acquires data for the run data store from one or morecorresponding instruments across a network connection.
 31. Thenon-transitory computer readable storage medium of claim 1, wherein thenon-transitory computer readable storage medium further storesinstructions for maintaining one or more interfaces for effectingprocess control, wherein each respective interface in the one or moreinterfaces controls one or more corresponding instruments associatedwith a process in the one or more processes.
 32. The non-transitorycomputer readable storage medium of claim 31, wherein a first interfacein the one or more interfaces controls a first instrument through thespecification of a process condition associated with the correspondingstage of the corresponding process.
 33. The non-transitory computerreadable storage medium of claim 1, wherein the first device is a singlecomputer system, a plurality of networked computer systems, or a virtualmachine.
 34. The non-transitory computer readable storage medium ofclaim 1, wherein a node in the plurality of nodes is not associated witha set of parameterized resource inputs.
 35. The non-transitory computerreadable storage medium of claim 1, wherein a node in the plurality ofnodes is not associated with a set of parameterized resource outputs.36. The non-transitory computer readable storage medium of claim 1,wherein two or more nodes in the plurality of nodes are each associatedwith a corresponding set of parameterized resource inputs.
 37. Thenon-transitory computer readable storage medium of claim 1, wherein twoor more nodes in the plurality of nodes are each associated with acorresponding set of parameterized resource outputs.
 38. A computersystem, comprising: one or more processors; memory; and one or moreprograms stored in the memory for execution by the one or moreprocessors, the one or more programs comprising instructions for: (A)maintaining a hypergraph data store comprising, for each respectiveprocess in a set of one or more processes, a respective plurality ofversions of the respective process, each process in the one or moreprocesses resulting in a respective product or analytical information,each respective version comprising: a hypergraph comprising a pluralityof nodes connected by edges in a plurality of edges, wherein eachrespective node in the plurality of nodes represents a respective stagein the respective process, a node in the plurality of nodes isassociated with (i) a set of parameterized resource inputs to therespective stage in the corresponding process, and/or (ii) a set ofparameterized resource outputs to the respective stage in thecorresponding process, and each respective edge in the plurality ofedges specifies that the set of parameterized resource outputs of a nodein the plurality of nodes is included in the set of parameterizedresource inputs of at least one other node in the plurality of nodes;(B) maintaining a run data store, wherein the run data store comprises aplurality of process runs, each process run comprising (i) anidentification of a version in the plurality of versions for a processin the one or more processes, (ii) values for the respective set ofparameterized resource inputs of a first node in the hypergraph of therespective version, (iii) the respective set of parameterized resourceoutputs of the first node in the hypergraph of the respective version,and (iv) obtained values of at least one output property of aparameterized resource output in the respective set of parameterizedresource outputs of the first node in the hypergraph of the respectiveversion; and (C) executing instructions for acquiring and processingdata for the one or more processes.
 39. The computer system of claim 38,wherein the computer system is in the form of a single computer system,a plurality of networked computer systems, or a virtual machine.